data("steno2")
data("st2clin")
data("st2alb")1 Purpose
I remember using the Epi::steno2 data-sets when taking a short course on multistate survival analysis with the creator of the package, Bendix Carstensen.
That was quite fun but also daunting to be sitting in a room full of mathematicians and people who actually knew the nitty gritty of survival analysis and the math behind it.
I think I would just want to do something very basic here, though:
- Playing around with joining the different data-sets
- Examine the data-sets and visualize the contents
- Creating a simple Cox Proportional Hazards model, comparing the risk of cardiovascular event between the two treatment groups in the data-set
- Probably also controlling for some important factors
- Deliver some sort of conclusion about these data
So lets start looking at it!
2 Load
The data-sets can be loaded directly from the Epi library, through data()
Alrighty now that they are loaded lets look at their structure. Ofcourse, I am completely ignoring the description in the documentation in the Epi() package, which describes how the data-sets are structured.
str(steno2)'data.frame': 160 obs. of 14 variables:
$ id : num 1 2 3 4 5 6 7 8 9 10 ...
$ allo : Factor w/ 2 levels "Int","Conv": 1 1 2 2 2 2 2 1 1 1 ...
$ sex : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 1 2 2 2 ...
$ baseCVD : num 0 0 0 0 0 1 0 0 0 0 ...
$ deathCVD: num 0 0 0 0 1 0 0 0 1 0 ...
$ doBth : Date, format: "1932-04-11" "1946-11-09" ...
$ doDM : Date, format: "1991-01-21" "1982-01-30" ...
$ doBase : Date, format: "1993-04-30" "1993-06-08" ...
$ doCVD1 : Date, format: "2014-05-24" "2009-03-15" ...
$ doCVD2 : Date, format: NA "2009-07-02" ...
$ doCVD3 : Date, format: NA "2010-02-16" ...
$ doESRD : Date, format: "NaN" "NaN" ...
$ doEnd : Date, format: "2014-10-12" "2014-08-04" ...
$ doDth : Date, format: NA NA ...str(st2clin)'data.frame': 750 obs. of 5 variables:
$ id : num 1 2 3 4 5 6 7 8 9 10 ...
$ doV : Date, format: "1993-05-07" "1993-05-05" ...
$ a1c : num 87.3 66.5 73 61.2 102.7 ...
$ chol: num 3.9 6.6 5.6 5.2 6 4.8 8.6 5.1 4.2 5.4 ...
$ crea: num 83 83 68 97 149 55 56 78 123 79 ...str(st2alb)'data.frame': 563 obs. of 3 variables:
$ id : num 1 1 1 1 1 2 2 2 2 2 ...
$ doTr : Date, format: "1993-06-12" "1995-05-13" ...
$ state: Factor w/ 3 levels "Norm","Mic","Mac": 2 1 2 1 2 1 2 3 2 2 ...It looks like steno2 and st2clin are one row per subject, and st2alb is multiple, but that doesn’t make sense given the amount of rows. We can see how many id’s, the unique identifier for each person, are present, and if there is one row per subject it should be close to the amount of subjects.
# create a 1 for each time there is an ID
id_test1 <- steno2 |> count(id)
head(id_test1) id n
1 1 1
2 2 1
3 3 1
4 4 1
5 5 1
6 6 1# different numbers on those counts
id_test2 <- id_test1 |> summarize(n=sum(n), idcount = max(id), sum = sum(id))
id_test2 n idcount sum
1 160 176 13650Alright. So it is clear that only steno2 has one row per subject because the number of id’s that occur equal the number of unique rows.
Lets see about the others
st2alb |> count(id) |> summarize(n=sum(n), idcount = max(id), sum = sum(id)) n idcount sum
1 563 176 13187st2clin |> count(id) |> summarize(n=sum(n), idcount = max(id), sum = sum(id)) n idcount sum
1 750 176 13650Ok so the st2* datasets have more than one row per subject, but there is a strange lower sum in st2alb than the other datasets.
Could it be that there are missing subjects in that dataset?
2.1 A) Joining
Now, first, what would the purpose be of joining these data-sets together?
There could be many reasons and I will touch upon some of them:
- adding demographic characteristics from one data-set to another to stratify analysis
- find things that are in one data-set but not in another
For example, the st2clin and st2alb data-sets do not have the categories for each id from the steno2 data-set, such as sex and allocation.
Also, continuing from the previous section, were there any missing people in st2alb? There was a possible indication of it, which can be explored with a filtering join, more specifically, an anti_join, using the anti_join() function.
anti_join() return all rows from x without a match in y
So let’s try to see if there are any missing.
anti_join(steno2, st2alb, by = "id") id allo sex baseCVD deathCVD doBth doDM doBase doCVD1
1 83 Int M 0 1 1930-12-23 1989-06-28 1992-12-22 1994-12-04
2 102 Int M 0 1 1935-04-09 1990-03-19 1993-07-17 1995-04-23
3 128 Conv M 0 0 1937-03-14 1989-02-16 1993-10-03 1995-11-13
4 150 Conv F 0 0 1946-02-18 1989-10-29 1993-12-24 <NA>
doCVD2 doCVD3 doESRD doEnd doDth
1 1994-12-12 <NA> NaN 1994-11-15 1994-12-19
2 <NA> <NA> NaN 1995-03-30 1995-05-02
3 2003-08-07 2004-05-31 1999-12-26 2005-05-19 2005-05-18
4 <NA> <NA> NaN 2014-08-27 <NA> [1] id allo sex baseCVD deathCVD doBth doDM doBase
[9] doCVD1 doCVD2 doCVD3 doESRD doEnd doDth
<0 rows> (or 0-length row.names)It seems that id 83, 102, 128, and 150 are missing from st2alb. They are not missing from st2clin - I checked in a hidden code chunk. Since it’s stated in the documentation that it’s a data-set about transitions between states of albuminuria, perhaps one could assume that these individuals simply did not transition?
3 Output
3.1 B) Table
Of course, the good old summary() function might be worth to do to get a good overview of the three data-sets. First we do steno2 demographics.
summary(steno2) id allo sex baseCVD deathCVD
Min. : 1.00 Int :80 F: 41 Min. :0.0000 Min. :0.0000
1st Qu.: 40.75 Conv:80 M:119 1st Qu.:0.0000 1st Qu.:0.0000
Median : 82.50 Median :0.0000 Median :0.0000
Mean : 85.31 Mean :0.1375 Mean :0.2375
3rd Qu.:130.25 3rd Qu.:0.0000 3rd Qu.:0.0000
Max. :176.00 Max. :1.0000 Max. :1.0000
doBth doDM doBase
Min. :1926-07-13 Min. :1962-06-21 Min. :1992-12-22
1st Qu.:1932-07-18 1st Qu.:1982-10-01 1st Qu.:1993-03-23
Median :1937-03-12 Median :1987-05-02 Median :1993-06-11
Mean :1938-06-03 Mean :1986-01-15 Mean :1993-07-22
3rd Qu.:1944-11-28 3rd Qu.:1990-05-03 3rd Qu.:1993-11-15
Max. :1956-04-07 Max. :1994-02-07 Max. :1994-06-14
doCVD1 doCVD2 doCVD3
Min. :1993-03-17 Min. :1994-08-05 Min. :1995-12-16
1st Qu.:1997-02-16 1st Qu.:1999-04-18 1st Qu.:2001-11-23
Median :2001-04-03 Median :2002-12-20 Median :2004-12-13
Mean :2001-12-18 Mean :2003-10-04 Mean :2005-01-11
3rd Qu.:2006-07-20 3rd Qu.:2008-07-20 3rd Qu.:2009-06-03
Max. :2014-05-30 Max. :2014-03-26 Max. :2012-07-21
NA's :41 NA's :84 NA's :117
doESRD doEnd doDth
Min. :1998-02-18 Min. :1994-11-16 Min. :1994-12-19
1st Qu.:2002-09-25 1st Qu.:2002-09-18 1st Qu.:2000-05-21
Median :2008-05-08 Median :2010-02-11 Median :2004-03-06
Mean :2006-07-17 Mean :2008-08-29 Mean :2004-05-24
3rd Qu.:2009-07-29 3rd Qu.:2014-09-25 3rd Qu.:2009-06-04
Max. :2014-07-07 Max. :2014-12-31 Max. :2014-02-16
NA's :145 NA's :67 But I kinda want to mainly do graphs here.
3.2 B) Graphs
3.2.1 Some distributions
Now, I love me some functions, and I feel like seeing the distribution of all the different date variables via histograms.
plot_histo_spam <- function(df,histovar) {
out <- ggplot(df) +
geom_histogram(mapping = aes( {{histovar}} ))
return(out)
}
plot_histo_spam(steno2,doBth)`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot_histo_spam(steno2,doDM)`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot_histo_spam(steno2,doBase)`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
But this should be doable in a smarter way. A facet_wrap() over each of the date variables..
plot_histospam_faceted <- function(data, prefix = "do") {
data |>
select(starts_with(prefix)) |>
pivot_longer(everything()) |> # everything() turns varnames into a column, and their values into a separate column
ggplot(aes(value)) +
geom_histogram(colour = "white") +
facet_wrap(~ name, scales = "free_x") + # one panel per variable
labs(x = NULL)
}
plot_histospam_faceted(steno2)`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 454 rows containing non-finite outside the scale range
(`stat_bin()`).
plot_histospam_faceted(st2clin)`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot_histospam_faceted(st2alb)`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Now those are some fine basic plots!
For steno2, it shows some slight right skewed doBase, and left-skewed doDM and doEnd. for the two st2* data-sets, it shows quite a right skew.
Basically:
- most of the signups to the trial occurred in the beginning
- most of the CVD (cardiovascular) and death events occurred evenly-ish
- most of the diabetes diagnoses occurred late
- not much ESRD (end-stage renal disease) occurred
3.2.2 Distributions by sex
Now I want to figure out how to do it while grouping the histograms by sex.
plot_histospam_faceted_sex <- function(data, prefix = "do") {
data |>
select(sex, starts_with(prefix)) |>
pivot_longer(-sex,
names_to = "name",
values_to = "value") |>
ggplot(aes(value, fill = sex)) +
geom_histogram(
position = "dodge",
colour = "white") +
facet_wrap(~ name, scales = "free_x") + # one panel per variable
labs(x = NULL)
}
plot_histospam_faceted_sex(steno2)`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 454 rows containing non-finite outside the scale range
(`stat_bin()`).
Ok. Not much of a difference at a glance.
3.2.3 Distributions by treatment groups
Perhaps it would be more relevant to see if there was some kind of difference between the two intervention groups?
plot_histospam_faceted_allo <- function(data, prefix = "do") {
data |>
select(allo, starts_with(prefix)) |>
pivot_longer(-allo,
names_to = "name",
values_to = "value") |>
ggplot(aes(value, fill = allo)) +
geom_histogram(
position = "identity",
alpha = 0.5,
colour = "white") +
facet_wrap(~ name, scales = "free_x") + # one panel per variable
labs(x = NULL)
}
plot_histospam_faceted_allo(steno2)`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 454 rows containing non-finite outside the scale range
(`stat_bin()`).
Ok so already here its possible to see some apparent differences between the two groups.
- the
Convallocation group, which must be those allocated to conventional treatment, whatever that was, have more events of CVD, especially of the third CVD event. As well as earlier diabetes mellitus (DM) diagnoses. This is probably an indication of the future results of the survival analysis. Intgroup, likely the intervention group, seems to have less deaths and later DM diagnoses
Remember, these are two groups of equal number, which can make it easier to conclude that there is a difference, at least initially and visually.
Now. With the help of some AI, I made the function below which can produce different types of facet_* plots , but I choose the facet_grid, because its interesting to look at.
steno2 |>
select(allo, starts_with("do")) |>
pivot_longer(-allo, names_to = "variable", values_to = "value") |>
ggplot(aes(value, fill = allo)) +
geom_histogram(position = "identity", colour = "white") +
facet_grid(rows = vars(allo), cols = vars(variable)) +
#scale_x_continuous(breaks = floor(min(value)):ceiling(max(value)),
# labels = scales::number_format(accuracy = 1)) +
labs(x = NULL, fill = "Allocation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = .5, hjust = 1))`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 454 rows containing non-finite outside the scale range
(`stat_bin()`).
This might be information overload to such a degree that not much is gained from it.
3.3 C) Model
To perform a basic Cox Proportional Hazards model, we can use the survival package.
I want to do the following, just because that’s what I learned in the olden PhD studying days, mainly from the Basic Biostats courses from Aarhus University:
- Survival set the data
- Visualise follow-up
- Plot Kaplan Meier survival function
- Cox model
- Estimate
- Observed vs fitted survival curves
- Testing proportional hazards assumption
Let’s just quickly and without much thinking (thats always good), decide what the start, stop, outcome, and other variables should be.
- data:
steno2- comparison: death
doDthfor intervention groupsallo - start date:
doBase - censor date:
doEnd - outcome event:
doDth - exit of study: earliest of
doEndanddoDth
- comparison: death
So, super basic, and does not take anything into account, such as competing events (doCVD* or doESRD) or baseline differences in risk of CVD (baseCVD).
3.3.1 Survival set the data
First we create the needed variables and check it.
steno2 <- steno2 |>
mutate(
doExit = coalesce(doDth, doEnd),
time_days = difftime(
coalesce(steno2$doDth, steno2$doEnd),
steno2$doBase,
units='days'),
time_yrs = as.numeric(time_days / 365.25),
event_num = if_else(!is.na(doDth),1,0),
event = factor(event_num, levels = c(0, 1), labels = c("Censored", "Dead"))
)
glimpse(steno2)Rows: 160
Columns: 19
$ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1…
$ allo <fct> Int, Int, Conv, Conv, Conv, Conv, Conv, Int, Int, Int, Int, …
$ sex <fct> M, M, M, M, M, M, F, M, M, M, M, M, M, M, F, M, F, F, M, M, …
$ baseCVD <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, …
$ deathCVD <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
$ doBth <date> 1932-04-11, 1946-11-09, 1943-06-07, 1944-11-01, 1935-12-22,…
$ doDM <date> 1991-01-21, 1982-01-30, 1982-07-17, 1976-10-28, 1986-05-06,…
$ doBase <date> 1993-04-30, 1993-06-08, 1993-05-17, 1993-05-04, 1993-03-23,…
$ doCVD1 <date> 2014-05-24, 2009-03-15, 2001-08-28, 1995-05-31, 1993-12-16,…
$ doCVD2 <date> NA, 2009-07-02, NA, 1997-06-26, 1994-08-05, NA, 2006-02-27,…
$ doCVD3 <date> NA, 2010-02-16, NA, 2003-06-17, 1997-12-25, NA, NA, NA, NA,…
$ doESRD <date> NaN, NaN, NaN, NaN, 1998-02-18, 2014-07-07, NaN, 2009-03-06…
$ doEnd <date> 2014-10-12, 2014-08-04, 2001-08-21, 2003-06-11, 1998-01-18,…
$ doDth <date> NA, NA, 2001-09-20, 2003-06-24, 1998-02-25, NA, 2006-04-24,…
$ doExit <date> 2014-10-12, 2014-08-04, 2001-09-20, 2003-06-24, 1998-02-25,…
$ time_days <drtn> 7834.3828 days, 7726.4177 days, 3048.4091 days, 3703.4893 d…
$ time_yrs <dbl> 21.449371, 21.153779, 8.346089, 10.139601, 4.929256, 21.4398…
$ event_num <dbl> 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, …
$ event <fct> Censored, Censored, Dead, Dead, Dead, Censored, Dead, Dead, …3.3.2 Visualise follow-up
Then, because I like it as an overview, a swimmer plot of the follow-up times.
simple first:
swim_plot_1 <- steno2 |>
mutate(id = fct_reorder(as.factor(id), time_yrs)) |>
ggplot(aes(x = doBase,
xend = doExit,
y = id)) +
geom_segment(color = "grey") +
geom_point(aes(x = doExit, shape = event)) +
geom_point(aes(x = doDth, shape = event))
swim_plot_1Warning: Removed 67 rows containing missing values or values outside the scale range
(`geom_point()`).
Looks good, but a tiny bit messy with the - although accurate - different starting times.
Lets do one with baseline time set to 0. This makes it easier to compare the follow-up times, which is more interesting for the analysis than calendar times.
The latter would probably be of more interest to an investigator trying to figure out when people signed up, or something.
swim_plot_2 <- steno2 |>
mutate(
id = fct_reorder(as.factor(id), time_yrs),
t0 = 0) |>
ggplot(aes(x = t0,
xend = time_yrs,
y = id)) +
geom_segment(color = "grey") +
geom_point(aes(x = time_yrs, shape = event, colour = event)) +
theme_minimal()
swim_plot_2
Here we have the x-axis in years since baseline.
Now, I have a sense that doExit and doDth are the same, meaning that plots that use doExit end up having two shapes for subjects that died.
How can I make it so that there are different shapes for each event? I can just take the previous plot and add to it.
swim_plot_3 <- swim_plot_2 +
scale_shape_manual(values = c(Censored = 17, Dead = 4)) +
scale_colour_manual(values = c(Censored = "black", Dead = "red"))
swim_plot_3
Ok that looks fine. Now I want to make the final plot for this and move on.
swim_plot_4 <- swim_plot_3 +
labs(
x = "Follow-up time in years since baseline",
y = "Subject sorted by follow-up",
title = "Follow-up per subject, years since baseline") +
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()
)
swim_plot_4
ggsave("thumbnail.jpg", plot = last_plot(), width = 6, height = 4) # saved as thumbnailA nice little plot showing an overview of the length of follow-up for the subjects in the study.
Not exactly sure how it is useful except for my own sake - I just like to know visually what my data looks like.
3.3.3 Disclaimer
I am not a statistician, so whatever is correct of below, is due to the wise words and lectures I have remembered from people who actually know what they are talking about. Many statistical results, especially when you wish to talk inference (X causes Y), require careful consideration of subjective priors, demographics, causal effects, and biases such as effect modifiers, confounders, selection bias, etc.
Now - with that out of the way: LETS CLAIM SOME THINGS!
3.3.4 Plot Kaplan Meier survival function
Now, less fiddling around with plots, and more looking at survival analysis.
The Kaplan meier estimator on a plot is wonderful for portraying differences in survival times. First step we have is to fit the data.
# Overall, by treatment allocation, subset into sex
km_os <- survfit(Surv(time_yrs, event_num) ~ 1, data = steno2)
km_allo <- survfit(Surv(time_yrs, event_num) ~ allo, data = steno2)
km_sex <- survfit(Surv(time_yrs, event_num) ~ sex, data = steno2)
km_allo_sex <- survfit(Surv(time_yrs, event_num) ~ allo + sex, data = steno2)
# Summarising, showing just the first 10 years
summary(km_os, times=c(0:10)) Call: survfit(formula = Surv(time_yrs, event_num) ~ 1, data = steno2)
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 160 0 1.000 0.00000 1.000 1.000
1 160 0 1.000 0.00000 1.000 1.000
2 158 2 0.988 0.00878 0.970 1.000
3 155 3 0.969 0.01376 0.942 0.996
4 154 1 0.963 0.01502 0.934 0.992
5 148 6 0.925 0.02082 0.885 0.967
6 143 5 0.894 0.02436 0.847 0.943
7 137 6 0.856 0.02774 0.804 0.912
8 131 6 0.819 0.03045 0.761 0.881
9 125 6 0.781 0.03268 0.720 0.848
10 116 9 0.725 0.03530 0.659 0.798summary(km_allo, times=c(0:10)) Call: survfit(formula = Surv(time_yrs, event_num) ~ allo, data = steno2)
allo=Int
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 80 0 1.000 0.0000 1.000 1.000
1 80 0 1.000 0.0000 1.000 1.000
2 78 2 0.975 0.0175 0.941 1.000
3 77 1 0.963 0.0212 0.922 1.000
4 76 1 0.950 0.0244 0.903 0.999
5 75 1 0.938 0.0271 0.886 0.992
6 73 2 0.913 0.0316 0.853 0.977
7 70 3 0.875 0.0370 0.805 0.951
8 66 4 0.825 0.0425 0.746 0.913
9 64 2 0.800 0.0447 0.717 0.893
10 63 1 0.788 0.0457 0.703 0.882
allo=Conv
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 80 0 1.000 0.0000 1.000 1.000
1 80 0 1.000 0.0000 1.000 1.000
2 80 0 1.000 0.0000 1.000 1.000
3 78 2 0.975 0.0175 0.941 1.000
4 78 0 0.975 0.0175 0.941 1.000
5 73 5 0.913 0.0316 0.853 0.977
6 70 3 0.875 0.0370 0.805 0.951
7 67 3 0.838 0.0412 0.760 0.922
8 65 2 0.813 0.0436 0.731 0.903
9 61 4 0.763 0.0476 0.675 0.862
10 53 8 0.663 0.0529 0.567 0.775summary(km_sex, times=c(0:10)) Call: survfit(formula = Surv(time_yrs, event_num) ~ sex, data = steno2)
sex=F
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 41 0 1.000 0.0000 1.000 1.000
1 41 0 1.000 0.0000 1.000 1.000
2 41 0 1.000 0.0000 1.000 1.000
3 40 1 0.976 0.0241 0.930 1.000
4 40 0 0.976 0.0241 0.930 1.000
5 40 0 0.976 0.0241 0.930 1.000
6 39 1 0.951 0.0336 0.888 1.000
7 37 2 0.902 0.0463 0.816 0.998
8 34 3 0.829 0.0588 0.722 0.953
9 31 3 0.756 0.0671 0.635 0.900
10 29 2 0.707 0.0711 0.581 0.861
sex=M
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 119 0 1.000 0.0000 1.000 1.000
1 119 0 1.000 0.0000 1.000 1.000
2 117 2 0.983 0.0118 0.960 1.000
3 115 2 0.966 0.0165 0.935 0.999
4 114 1 0.958 0.0184 0.923 0.995
5 108 6 0.908 0.0266 0.857 0.961
6 104 4 0.874 0.0304 0.816 0.936
7 100 4 0.840 0.0336 0.777 0.909
8 97 3 0.815 0.0356 0.748 0.888
9 94 3 0.790 0.0373 0.720 0.867
10 87 7 0.731 0.0406 0.656 0.815summary(km_allo_sex, times=c(0:10)) Call: survfit(formula = Surv(time_yrs, event_num) ~ allo + sex, data = steno2)
allo=Int, sex=F
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 17 0 1.000 0.0000 1.000 1
1 17 0 1.000 0.0000 1.000 1
2 17 0 1.000 0.0000 1.000 1
3 17 0 1.000 0.0000 1.000 1
4 17 0 1.000 0.0000 1.000 1
5 17 0 1.000 0.0000 1.000 1
6 17 0 1.000 0.0000 1.000 1
7 17 0 1.000 0.0000 1.000 1
8 15 2 0.882 0.0781 0.742 1
9 14 1 0.824 0.0925 0.661 1
10 14 0 0.824 0.0925 0.661 1
allo=Int, sex=M
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 63 0 1.000 0.0000 1.000 1.000
1 63 0 1.000 0.0000 1.000 1.000
2 61 2 0.968 0.0221 0.926 1.000
3 60 1 0.952 0.0268 0.901 1.000
4 59 1 0.937 0.0307 0.878 0.999
5 58 1 0.921 0.0341 0.856 0.990
6 56 2 0.889 0.0396 0.815 0.970
7 53 3 0.841 0.0460 0.756 0.937
8 51 2 0.810 0.0495 0.718 0.913
9 50 1 0.794 0.0510 0.700 0.900
10 49 1 0.778 0.0524 0.682 0.888
allo=Conv, sex=F
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 24 0 1.000 0.0000 1.000 1.000
1 24 0 1.000 0.0000 1.000 1.000
2 24 0 1.000 0.0000 1.000 1.000
3 23 1 0.958 0.0408 0.882 1.000
4 23 0 0.958 0.0408 0.882 1.000
5 23 0 0.958 0.0408 0.882 1.000
6 22 1 0.917 0.0564 0.813 1.000
7 20 2 0.833 0.0761 0.697 0.997
8 19 1 0.792 0.0829 0.645 0.972
9 17 2 0.708 0.0928 0.548 0.916
10 15 2 0.625 0.0988 0.458 0.852
allo=Conv, sex=M
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 56 0 1.000 0.0000 1.000 1.000
1 56 0 1.000 0.0000 1.000 1.000
2 56 0 1.000 0.0000 1.000 1.000
3 55 1 0.982 0.0177 0.948 1.000
4 55 0 0.982 0.0177 0.948 1.000
5 50 5 0.893 0.0413 0.815 0.978
6 48 2 0.857 0.0468 0.770 0.954
7 47 1 0.839 0.0491 0.748 0.941
8 46 1 0.821 0.0512 0.727 0.928
9 44 2 0.786 0.0548 0.685 0.901
10 38 6 0.679 0.0624 0.567 0.813Here we can see that after 10 years, there is already a lower survival probability for the conventional treatment group. And for females, it seems. But that highly depends on the intervention group.
But it is much better to visualize it with a kaplan meier plot, to really see the differences, and when - in terms of time since baseline - does the survival differ.
Shown below are plots for each of the different fits mentioned above, showing the overall survival modelel, differences in survival between intervention groups for both sexes, and ditto, but subset for each sex.
# Function to save some space
survplot_function <- function(fit,data){
out <- ggsurvplot(
fit, data = data,
conf.int = TRUE,
risk.table = TRUE,
palette = "dark2",
xlab = NULL, ylab = "Survival Probability",
title = "Kaplan Meier Estimator for survival probability, in years"
)
return(out)
}
# Overall
survplot_function(km_os,steno2)
# allo
survplot_function(km_allo,steno2)
# sex
survplot_function(km_sex,steno2)
# allo sex
survplot_function(km_allo_sex,steno2)
Thats a bit rough to look at, but provides plenty of information:
- intervention group has higher survival probability
- female sex seems to indicate having the highest survival probability, at least in intervention group
- frequently overlapping confidence intervals, low probability of actual difference atleast in the first 10 years where most of the overlap occurs
For the second point, the lines cross twice, either because of the lower number of females or because of some unknown factor. When the lines cross its difficult to say whether there is any consistent survival difference with certainty.
But let’s try to make it pretty - just with the help of the survminer package help - accessed in Rstudio via ?ggsurvplot.
# allo
surv_p_allo_pretty <- ggsurvplot(
fit = km_allo, data = steno2,
surv.median.line = "hv", # Add medians survival
legend.title = "Allocation group",
legend.labs = c("Intervention", "Conventional"),
pval = TRUE,
conf.int = TRUE,
risk.table = TRUE,
tables.height = 0.2,
tables.theme = theme(axis.title.y = element_blank()),
palette = "dark2",
ggtheme = theme_bw(),
xlab = NULL, ylab = "Survival Probability",
title = "Kaplan Meier Estimator for survival probability, in years"
)
surv_p_allo_sex_pretty <- ggsurvplot(
fit = km_allo_sex, data = steno2,
surv.median.line = "hv", # Add medians survival
legend.title = "Allocation group, Sex",
legend.labs = c("Int, F", "Int, M", "Conv, F", "Conv, M"),
conf.int = TRUE,
risk.table = TRUE,
tables.height = 0.2,
tables.theme = theme(axis.title.y = element_blank()),
palette = "dark2",
ggtheme = theme_bw(),
xlab = NULL, ylab = "Survival Probability",
title = "Kaplan Meier Estimator for survival probability, in years"
)Warning in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : All aesthetics have length 1, but the data has 3 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.surv_p_allo_pretty
surv_p_allo_sex_prettyWarning in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : All aesthetics have length 1, but the data has 3 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.
All aesthetics have length 1, but the data has 3 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.
All aesthetics have length 1, but the data has 3 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.
Now slightly cleaned up, and with some median survival lines - the follow-up time at which 50 has experienced the event - added.
3.3.4.1 Test of differences
One way of testing the differences between two survival curves is the log-rank test. Although, as far as I remember, its not given much weight - especially compared to visual inspection coupled with careful thinking and analysis of the different factors that play into a survival analysis.
survdiff(Surv(time_yrs, event_num) ~ allo, data = steno2)Call:
survdiff(formula = Surv(time_yrs, event_num) ~ allo, data = steno2)
N Observed Expected (O-E)^2/E (O-E)^2/V
allo=Int 80 38 51.7 3.64 8.29
allo=Conv 80 55 41.3 4.56 8.29
Chisq= 8.3 on 1 degrees of freedom, p= 0.004 survdiff(Surv(time_yrs, event_num) ~ sex, data = steno2)Call:
survdiff(formula = Surv(time_yrs, event_num) ~ sex, data = steno2)
N Observed Expected (O-E)^2/E (O-E)^2/V
sex=F 41 20 25.5 1.182 1.63
sex=M 119 73 67.5 0.446 1.63
Chisq= 1.6 on 1 degrees of freedom, p= 0.2 Seems there is a statistically significant difference between allocation groups and not sex. Kinda what I expected from the reading of the plots.
I have forgotten how to interpret the log-rank model for a fit with two different binary variables, so I will refrain from attempting that at the moment.
3.3.5 Cox model
Now we enter the part of survival analysis where we want to see what effect sizes the different predictors have on the outcome.
As mentioned in Section 3.3.3, I am not a statistician, and I do not think the target audience of 0 to 1/3 of a person should be a statistician, so I will not bombard You with all the equations and definitions of survival analysis, such as \(S(t) = P(T>t) = etc..etc\).
I will just try to write in plain terms.
Where the survival function derived from the Kaplan Meier Estimator up above could tell us how many people have survived at time \(t\), and the difference between groups, the cox proportional hazards model can describe the relative difference in effect between different predictors, in regards to the risk of the outcome.
Spoken plainly: the cox proportional hazards model can tell us something about how much the risk of the outcome, doDth, increases or decreases, for those in one group compared to another group. And then if there are multiple variables in the analysis, its assumed all other variables stay the same.
Let’s do the same models as for the survival function.
# Overall, by treatment allocation, subset into sex
cox_allo <- coxph(Surv(time_yrs, event_num) ~ allo, data = steno2)
cox_sex <- coxph(Surv(time_yrs, event_num) ~ sex, data = steno2)
cox_allo_sex <- coxph(Surv(time_yrs, event_num) ~ allo + sex, data = steno2)3.3.5.1 Checking models out before interpreting estimates
Before we go ahead and interpret estimates, it is recommended that you see how the data fits with the assumptions that are necessary for the cox model to be interpreted correctly.
In general, I have been taught to test the cox proportional hazards assumption, which can be done in two ways:
- plot observed survival curves in the two groups with estimated survival curves from the model
- log log plot of two survival curves
plot(km_allo,
col=1:2,
lty=2,
main='Observed and fitted survival')
lines(survfit(cox_allo,
newdata=data.frame(allo=c('Int','Conv'))),
col=1:2)
legend('bottomleft',
lty=rep(1:2,2),col=rep(1:2,each=2),
legend=c('Int, fit','Int, obs','Conv, fit','Conv, obs'))
plot(km_allo,
col=1:2,
fun='cloglog',
main='Log-log plot')
legend('topleft',lty=rep(1,2),col=1:2,legend=levels(steno2$allo))
Hmm. I also wanted to use ggsurvplot but for some reason, I can only get the old code, without risk table, from my course long ago in basic biostats to work, so I will use that.
But more importantly:
Basically what we can see from the two above plots is that the proportional hazards assumption doesn’t hold well, due to crossover of survival curves and perhaps a slight possibility of different baseline hazards.
If the hazards for the groups being compared are not relatively proportional over time, its estimates do not precisely answer the question of “how much more risk is one group in of the outcome versus the other”, since it varies over time.
It answers a general tendency, which might switch at some point in the data, as can be seen by earlier plots where observed survival curves overlap early.
3.3.5.2 Estimates
But lets still look at the model estimates and interpret them a bit, but bearing in mind that the proportional hazards assumption doesn’t hold.
For a more simple output of the models, I use broom::tidy().
# list
cox_models <- list(cox_allo,cox_sex,cox_allo_sex)
names(cox_models) <- paste0("model_", seq_along(cox_models))
# tidy
tidy_cox_models <- map_dfr(cox_models, ~broom::tidy(.x, conf.int = TRUE))
tidy_cox_models# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 alloConv 0.602 0.212 2.84 0.00455 0.186 1.02
2 sexM 0.321 0.253 1.27 0.203 -0.174 0.816
3 alloConv 0.628 0.213 2.95 0.00316 0.211 1.05
4 sexM 0.379 0.253 1.50 0.135 -0.118 0.875tidy_cox_models_exp <- map_dfr(cox_models, ~broom::tidy(.x, conf.int = TRUE, exponentiate = TRUE))
tidy_cox_models_exp# A tibble: 4 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 alloConv 1.83 0.212 2.84 0.00455 1.20 2.77
2 sexM 1.38 0.253 1.27 0.203 0.840 2.26
3 alloConv 1.87 0.213 2.95 0.00316 1.23 2.84
4 sexM 1.46 0.253 1.50 0.135 0.889 2.40Where the first two rows are the first two models, and row three to four is the model with both variables, so you can see how much of an effect it has if you include them both.
Let’s go ahead and do a simple interpretation on the model with both allocation and sex as predictors.
Using broom::augment, one can plop the estimates into a table as an overview of what the model estimates mean for each variable.
cox_grid_1 <- expand.grid(allo = c("Conv", "Int"))
cox_grid_2 <- expand.grid(allo = c("Conv", "Int"), sex = c("M","F"))
(broom::augment(cox_allo,newdata = cox_grid_1, type.predict = "risk"))# A tibble: 2 × 3
allo .fitted .se.fit
<fct> <dbl> <dbl>
1 Conv 1.83 0.287
2 Int 1 0 (broom::augment(cox_allo_sex,newdata = cox_grid_2, type.predict = "risk"))# A tibble: 4 × 4
allo sex .fitted .se.fit
<fct> <fct> <dbl> <dbl>
1 Conv M 2.74 0.567
2 Int M 1.46 0.306
3 Conv F 1.87 0.291
4 Int F 1 0 Due to the type.predict option, the result is exponentiated so it can be interpreted as risk increase.
So, the model output for cox_allo_sex can be interpreted in the following way:
Females on intervention treatment are the reference group, and females with conventional treatment have 87% higher risk of death, while males on intervention treatment have 46%, and males on conventional 174% higher risk.
4 Summary / cleaned up / refactored
Rows: 160
Columns: 14
$ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
$ allo <fct> Int, Int, Conv, Conv, Conv, Conv, Conv, Int, Int, Int, Int, C…
$ sex <fct> M, M, M, M, M, M, F, M, M, M, M, M, M, M, F, M, F, F, M, M, F…
$ baseCVD <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0…
$ deathCVD <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ doBth <date> 1932-04-11, 1946-11-09, 1943-06-07, 1944-11-01, 1935-12-22, …
$ doDM <date> 1991-01-21, 1982-01-30, 1982-07-17, 1976-10-28, 1986-05-06, …
$ doBase <date> 1993-04-30, 1993-06-08, 1993-05-17, 1993-05-04, 1993-03-23, …
$ doCVD1 <date> 2014-05-24, 2009-03-15, 2001-08-28, 1995-05-31, 1993-12-16, …
$ doCVD2 <date> NA, 2009-07-02, NA, 1997-06-26, 1994-08-05, NA, 2006-02-27, …
$ doCVD3 <date> NA, 2010-02-16, NA, 2003-06-17, 1997-12-25, NA, NA, NA, NA, …
$ doESRD <date> NaN, NaN, NaN, NaN, 1998-02-18, 2014-07-07, NaN, 2009-03-06,…
$ doEnd <date> 2014-10-12, 2014-08-04, 2001-08-21, 2003-06-11, 1998-01-18, …
$ doDth <date> NA, NA, 2001-09-20, 2003-06-24, 1998-02-25, NA, 2006-04-24, …
Rows: 750
Columns: 5
$ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19…
$ doV <date> 1993-05-07, 1993-05-05, 1993-05-11, 1993-05-12, 1993-02-25, 1993…
$ a1c <dbl> 87.3, 66.5, 73.0, 61.2, 102.7, 93.4, 94.5, 103.9, 46.4, 84.4, 47.…
$ chol <dbl> 3.9, 6.6, 5.6, 5.2, 6.0, 4.8, 8.6, 5.1, 4.2, 5.4, 8.9, 5.3, 6.0, …
$ crea <dbl> 83, 83, 68, 97, 149, 55, 56, 78, 123, 79, 74, 84, 65, 52, 61, 61,…
Rows: 563
Columns: 3
$ id <dbl> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 6, 6, 6, 6…
$ doTr <date> 1993-06-12, 1995-05-13, 2000-01-26, 2001-12-26, 2007-04-27, 199…
$ state <fct> Mic, Norm, Mic, Norm, Mic, Norm, Mic, Mac, Mic, Mic, Mic, Mic, M…[[1]]
id allo sex baseCVD deathCVD doBth doDM doBase doCVD1
1 1 Int M 0 0 1932-04-11 1991-01-21 1993-04-30 2014-05-24
2 2 Int M 0 0 1946-11-09 1982-01-30 1993-06-08 2009-03-15
3 3 Conv M 0 0 1943-06-07 1982-07-17 1993-05-17 2001-08-28
4 4 Conv M 0 0 1944-11-01 1976-10-28 1993-05-04 1995-05-31
5 5 Conv M 0 1 1935-12-22 1986-05-06 1993-03-23 1993-12-16
6 6 Conv M 1 0 1946-12-23 1986-06-03 1993-03-31 1998-10-21
7 7 Conv F 0 0 1929-02-17 1977-12-18 1993-03-28 2000-12-27
8 8 Int M 0 0 1932-10-30 1982-10-26 1993-04-10 2011-11-16
9 9 Int M 0 1 1930-06-26 1962-06-21 1993-03-22 1995-01-21
10 10 Int M 0 0 1937-06-28 1983-01-24 1993-01-09 2011-05-23
11 11 Int M 0 1 1930-02-08 1991-10-27 1993-02-11 2005-08-27
12 12 Conv M 1 1 1928-04-03 1981-05-12 1993-03-11 1993-12-14
13 13 Int M 0 0 1945-09-07 1990-03-23 1993-03-01 <NA>
14 14 Int M 0 0 1929-10-08 1981-03-26 1993-02-10 2014-02-09
15 15 Int F 0 0 1936-03-29 1988-06-10 1993-01-05 <NA>
16 16 Int M 1 0 1934-12-14 1987-07-18 1993-01-19 2000-01-20
17 17 Int F 0 0 1942-12-16 1984-10-27 1993-06-11 <NA>
18 18 Conv F 0 0 1939-04-11 1966-06-11 1993-06-08 1998-04-05
19 19 Conv M 0 0 1945-12-29 1992-10-21 1993-06-02 <NA>
20 20 Conv M 0 0 1946-05-08 1988-07-27 1993-06-07 <NA>
21 21 Conv F 0 0 1935-09-16 1981-11-03 1993-06-11 1994-05-05
22 22 Int F 0 0 1933-07-13 1975-08-11 1993-05-16 <NA>
23 23 Conv M 0 1 1934-06-19 1980-02-22 1993-01-12 1993-03-17
24 24 Int M 0 0 1937-06-16 1989-08-04 1993-03-11 2005-05-28
25 25 Conv M 0 0 1935-09-04 1991-01-17 1993-04-04 <NA>
26 26 Conv M 0 1 1929-08-18 1990-03-17 1993-04-09 1999-09-18
27 27 Conv M 0 0 1931-03-02 1983-02-17 1993-04-16 2002-07-29
28 28 Int M 0 0 1945-09-26 1990-03-14 1993-03-13 <NA>
29 29 Conv M 0 1 1939-03-11 1969-08-03 1993-03-26 2005-02-22
30 30 Int M 1 0 1932-07-09 1982-05-02 1993-05-03 2004-05-04
31 31 Conv F 0 1 1942-06-30 1986-04-29 1993-03-12 2006-02-26
32 32 Conv F 0 0 1944-10-25 1991-10-01 1993-03-21 2000-02-23
33 33 Int M 0 0 1941-05-03 1988-10-14 1993-03-16 <NA>
34 34 Int M 0 0 1941-10-30 1990-02-07 1993-02-28 1996-01-06
35 35 Conv F 0 1 1928-08-29 1991-09-19 1993-06-30 2001-08-23
36 36 Conv F 1 0 1947-02-12 1964-02-07 1993-04-14 2001-12-02
37 37 Int M 0 0 1948-02-01 1987-03-27 1993-01-24 2008-09-28
38 38 Conv M 0 1 1932-06-14 1986-10-14 1993-03-07 1998-09-05
39 39 Int M 0 0 1944-06-23 1985-07-10 1993-01-20 2010-10-27
40 40 Conv F 0 1 1937-12-12 1984-08-12 1993-04-02 1997-09-08
41 41 Int M 0 0 1928-11-12 1991-06-28 1992-12-29 2002-06-26
42 42 Conv M 0 1 1936-02-03 1974-11-23 1993-03-12 2002-10-01
43 43 Conv M 1 0 1930-08-28 1979-03-04 1993-01-14 1994-01-29
44 44 Conv M 1 0 1948-06-14 1985-04-28 1993-02-23 <NA>
45 45 Conv M 0 0 1945-08-27 1990-11-01 1993-02-28 2010-03-17
46 47 Conv F 0 0 1937-05-25 1974-12-14 1993-04-30 1997-05-15
47 48 Conv F 0 0 1926-09-12 1984-07-19 1993-06-24 2010-11-04
48 50 Conv M 0 1 1929-12-21 1993-04-18 1993-05-28 2002-07-06
49 51 Int M 0 0 1946-05-20 1981-09-04 1993-06-04 2010-07-20
50 52 Int M 0 0 1944-07-14 1989-07-01 1993-02-03 1996-12-01
51 53 Conv F 0 0 1927-06-17 1982-07-04 1993-03-05 1996-12-16
52 54 Int F 1 0 1945-04-26 1989-06-21 1993-02-26 <NA>
53 55 Int F 0 0 1948-04-10 1987-05-13 1993-04-15 <NA>
54 56 Int M 0 0 1932-09-16 1989-03-06 1993-03-11 2012-04-16
55 57 Conv F 0 0 1928-11-22 1974-02-16 1993-02-05 1995-08-17
56 58 Conv M 0 0 1938-03-11 1987-08-26 1993-02-04 2005-05-22
57 59 Int M 0 0 1929-04-17 1985-10-31 1993-07-02 2004-10-19
58 60 Int M 0 1 1936-02-27 1986-04-05 1993-06-24 1997-09-22
59 61 Conv M 0 0 1941-06-28 1986-08-29 1993-05-16 <NA>
60 62 Conv F 0 1 1935-01-09 1985-07-16 1993-06-22 1999-04-06
61 63 Conv M 0 1 1931-10-21 1987-07-25 1993-04-04 1999-01-01
62 64 Conv M 0 0 1944-06-07 1990-01-01 1993-03-19 2006-10-22
63 65 Int F 0 0 1946-01-30 1975-02-02 1993-05-05 <NA>
64 66 Conv M 0 1 1932-10-14 1979-09-30 1993-03-01 1995-12-23
65 67 Int F 0 0 1927-08-15 1978-08-23 1993-05-11 1994-02-11
66 68 Int M 0 0 1947-06-18 1989-10-11 1993-03-30 2000-10-09
67 69 Int M 0 0 1933-01-08 1991-11-08 1993-03-04 <NA>
68 70 Conv M 0 0 1934-10-24 1978-10-12 1993-01-25 1995-03-26
69 71 Conv M 0 0 1937-03-10 1982-05-14 1993-05-18 2004-08-21
70 72 Conv M 0 0 1947-10-27 1990-09-03 1993-05-03 <NA>
71 73 Int M 0 0 1930-04-17 1977-02-16 1993-01-22 1997-01-29
72 74 Int F 1 0 1928-08-14 1974-05-16 1993-05-12 1997-03-06
73 75 Conv F 0 1 1935-03-04 1981-01-21 1993-03-19 2008-11-21
74 76 Int M 0 0 1936-06-29 1980-04-16 1993-02-17 1996-09-05
75 77 Int M 0 0 1938-04-03 1991-10-10 1993-03-23 <NA>
76 78 Conv M 0 0 1928-05-30 1985-07-21 1993-04-08 <NA>
77 79 Int M 0 1 1931-12-03 1974-02-14 1993-04-07 1994-10-31
78 80 Conv M 0 0 1940-07-03 1990-03-22 1993-04-04 2008-05-21
79 81 Int M 0 1 1943-12-17 1990-03-22 1993-03-21 1999-12-18
80 82 Int M 1 0 1927-09-07 1990-10-13 1993-01-02 1999-02-24
81 83 Int M 0 1 1930-12-23 1989-06-28 1992-12-22 1994-12-04
82 84 Int F 0 0 1932-07-21 1983-11-16 1993-03-29 2009-04-03
83 85 Conv M 0 0 1943-05-22 1987-04-22 1993-03-31 2009-09-26
84 86 Int F 0 0 1932-11-18 1973-04-09 1993-06-01 <NA>
85 87 Int M 0 1 1933-03-21 1984-06-25 1993-04-20 1993-10-17
86 88 Conv M 0 0 1942-01-13 1986-05-16 1993-02-15 1998-01-18
87 89 Int M 1 0 1935-08-18 1992-06-30 1993-06-30 1995-05-20
88 90 Conv M 0 1 1947-01-22 1988-10-16 1993-07-27 2001-07-03
89 91 Int M 0 0 1930-10-24 1990-03-02 1993-08-23 2001-08-05
90 92 Int M 1 0 1946-03-10 1992-09-02 1993-08-25 <NA>
91 93 Conv M 0 0 1951-05-04 1980-07-10 1993-08-20 1998-08-22
92 94 Conv M 0 1 1940-03-21 1993-05-14 1993-07-19 2007-09-24
93 95 Int M 0 0 1938-01-14 1992-02-25 1993-09-01 2004-03-20
94 96 Int M 0 1 1942-02-13 1988-05-17 1993-08-26 1998-12-31
95 97 Int F 0 0 1946-05-12 1992-12-19 1993-08-24 <NA>
96 98 Int M 0 1 1933-09-20 1975-08-29 1993-08-27 1997-05-04
97 100 Int M 1 0 1949-11-13 1985-12-15 1993-09-10 <NA>
98 101 Int M 0 0 1936-06-03 1988-12-28 1994-02-07 <NA>
99 102 Int M 0 1 1935-04-09 1990-03-19 1993-07-17 1995-04-23
100 103 Int M 0 0 1932-10-18 1981-07-20 1993-08-28 <NA>
101 104 Int M 0 0 1946-12-12 1986-02-02 1993-07-22 2014-05-30
102 105 Int M 1 0 1949-01-14 1993-12-06 1994-01-15 <NA>
103 107 Int F 0 0 1936-07-26 1992-04-10 1993-09-01 2001-06-30
104 108 Int M 0 0 1956-04-07 1992-05-20 1993-08-28 <NA>
105 110 Int M 0 0 1934-07-13 1989-01-27 1993-09-14 2009-12-09
106 111 Int M 0 0 1951-05-15 1993-05-30 1993-08-16 <NA>
107 112 Conv F 0 0 1949-01-18 1988-09-06 1993-11-02 <NA>
108 113 Conv F 0 0 1931-05-19 1966-10-24 1993-10-05 1999-02-17
109 114 Conv M 0 0 1934-07-21 1982-11-05 1993-10-02 1998-06-01
110 115 Conv M 0 0 1940-11-12 1984-05-15 1993-10-30 <NA>
111 116 Conv M 0 1 1928-09-06 1987-05-12 1993-09-12 1998-07-22
112 118 Conv M 1 1 1933-09-07 1988-10-20 1993-10-09 2006-04-18
113 119 Conv M 0 0 1949-08-17 1993-03-07 1993-09-14 1998-02-28
114 120 Conv M 0 0 1939-04-06 1984-01-23 1993-10-27 1996-06-20
115 121 Conv M 1 0 1927-04-30 1978-01-22 1993-11-16 1997-01-28
116 123 Int M 0 0 1948-03-02 1991-01-13 1993-10-10 2013-02-04
117 126 Int F 0 0 1934-07-27 1986-06-28 1993-10-21 <NA>
118 128 Conv M 0 0 1937-03-14 1989-02-16 1993-10-03 1995-11-13
119 129 Conv M 0 1 1936-06-24 1988-07-23 1993-10-17 2002-11-20
120 130 Conv M 0 1 1939-07-01 1980-11-23 1993-10-15 1996-01-16
121 131 Conv M 1 0 1931-10-07 1992-02-15 1993-11-27 2003-12-18
122 133 Int M 0 0 1945-08-12 1992-09-24 1994-02-21 2007-12-24
123 134 Conv F 0 1 1930-01-31 1980-08-28 1993-11-14 1994-12-01
124 135 Conv F 0 0 1931-07-05 1993-04-16 1993-10-12 1997-06-05
125 136 Conv M 0 0 1949-10-23 1983-03-15 1993-12-16 2000-06-12
126 137 Conv M 0 0 1932-09-11 1993-09-04 1993-10-31 1998-09-07
127 138 Conv F 0 0 1947-10-05 1993-04-24 1993-12-14 <NA>
128 139 Conv M 0 0 1949-05-08 1988-12-30 1993-11-22 1995-07-14
129 140 Conv M 1 0 1951-04-14 1987-08-10 1993-11-28 2001-04-03
130 142 Int M 0 0 1942-05-15 1989-06-26 1993-12-03 2007-09-04
131 143 Int M 0 0 1936-10-31 1985-11-01 1993-12-12 2002-05-30
132 144 Conv M 1 0 1932-11-21 1987-04-20 1993-12-19 2001-05-17
133 145 Int M 0 1 1936-11-21 1985-12-17 1994-02-01 1999-04-25
134 146 Conv M 0 1 1944-05-25 1983-11-18 1994-01-31 1994-07-17
135 147 Int M 1 0 1945-02-16 1994-01-04 1994-01-04 2012-08-25
136 148 Conv F 0 0 1938-12-29 1986-04-07 1993-12-26 2012-06-20
137 150 Conv F 0 0 1946-02-18 1989-10-29 1993-12-24 <NA>
138 151 Conv M 0 1 1929-02-04 1988-05-12 1994-01-25 2004-02-28
139 152 Int F 1 0 1947-11-08 1993-06-08 1994-01-13 2009-04-09
140 153 Int M 0 0 1934-10-31 1985-04-01 1994-02-17 1995-09-22
141 155 Conv M 0 0 1949-11-21 1994-02-07 1994-02-12 <NA>
142 156 Int M 0 0 1942-09-10 1993-05-13 1994-02-10 2002-04-25
143 157 Conv M 0 1 1938-03-08 1987-05-24 1994-02-27 2007-07-03
144 158 Int M 1 0 1931-06-17 1986-09-07 1994-01-22 2009-12-10
145 159 Int M 0 1 1926-07-13 1990-10-20 1994-01-10 2010-06-08
146 160 Conv M 0 0 1928-06-20 1986-12-03 1994-02-02 1994-12-13
147 161 Int M 0 0 1939-03-16 1989-11-10 1994-02-25 <NA>
148 163 Int M 0 0 1929-12-31 1990-11-12 1994-01-24 2002-02-17
149 165 Conv F 0 0 1930-08-16 1992-09-29 1994-02-21 2002-04-04
150 166 Conv M 0 1 1936-09-03 1986-08-23 1994-01-25 2007-04-19
151 167 Int F 0 0 1952-11-12 1980-07-07 1994-03-09 <NA>
152 168 Conv M 0 0 1937-05-19 1986-03-12 1994-02-13 2002-03-10
153 169 Int M 0 0 1946-10-08 1993-09-03 1994-05-12 <NA>
154 170 Int F 0 0 1933-04-02 1991-10-24 1994-05-19 <NA>
155 171 Int M 0 0 1939-07-02 1992-09-22 1994-04-23 <NA>
156 172 Conv F 0 0 1950-11-23 1983-09-27 1994-05-05 2011-12-25
157 173 Int F 0 0 1943-10-29 1982-07-03 1994-05-19 <NA>
158 174 Int M 0 0 1931-10-27 1989-04-01 1994-06-14 <NA>
159 175 Int M 0 0 1936-12-26 1991-12-17 1994-05-06 1998-10-01
160 176 Conv F 0 0 1943-02-12 1987-10-23 1994-05-25 <NA>
doCVD2 doCVD3 doESRD doEnd doDth
1 <NA> <NA> NaN 2014-10-12 <NA>
2 2009-07-02 2010-02-16 NaN 2014-08-04 <NA>
3 <NA> <NA> NaN 2001-08-21 2001-09-20
4 1997-06-26 2003-06-17 NaN 2003-06-11 2003-06-24
5 1994-08-05 1997-12-25 1998-02-18 1998-01-18 1998-02-25
6 <NA> <NA> 2014-07-07 2014-09-07 <NA>
7 2006-02-27 <NA> NaN 2006-03-28 2006-04-24
8 2012-02-12 <NA> 2009-03-06 2012-02-25 2012-02-27
9 1995-11-25 <NA> NaN 1995-12-14 1995-12-05
10 <NA> <NA> NaN 2011-05-28 2011-05-30
11 2006-01-23 <NA> 2006-01-11 2006-01-26 2006-02-03
12 1995-04-27 2002-05-16 NaN 2002-09-28 2002-09-15
13 <NA> <NA> NaN 2014-09-25 <NA>
14 <NA> <NA> NaN 2014-01-15 2014-02-16
15 <NA> <NA> NaN 2014-12-01 <NA>
16 2002-03-05 2009-08-30 NaN 2009-09-05 2009-09-07
17 <NA> <NA> NaN 2014-11-01 <NA>
18 1998-06-11 1998-08-20 NaN 2014-12-30 <NA>
19 <NA> <NA> NaN 2014-12-30 <NA>
20 <NA> <NA> NaN 2014-09-02 <NA>
21 <NA> <NA> NaN 2014-10-16 <NA>
22 <NA> <NA> NaN 2014-10-13 <NA>
23 1998-12-22 <NA> NaN 1998-12-30 1998-12-29
24 2007-09-07 2010-03-02 NaN 2010-02-26 2010-03-09
25 <NA> <NA> NaN 2014-08-14 <NA>
26 2000-01-20 2001-11-09 NaN 2001-11-09 2001-11-16
27 2006-02-15 2006-05-07 NaN 2009-03-25 2009-03-17
28 <NA> <NA> NaN 2014-08-06 <NA>
29 <NA> <NA> NaN 2005-03-22 2005-03-23
30 2005-08-11 <NA> NaN 2005-07-18 2005-08-22
31 2007-04-04 2008-07-03 NaN 2010-05-06 2010-04-21
32 <NA> <NA> NaN 2000-03-03 2000-03-01
33 <NA> <NA> NaN 2014-11-01 <NA>
34 2005-05-28 2012-07-21 NaN 2014-09-29 <NA>
35 2002-11-01 2002-12-14 2003-10-23 2005-02-02 2005-03-09
36 <NA> <NA> NaN 2014-11-26 <NA>
37 <NA> <NA> NaN 2008-09-12 2008-10-05
38 2000-03-22 2000-05-08 NaN 2003-04-28 2003-04-17
39 <NA> <NA> NaN 2014-08-27 <NA>
40 2000-01-13 2000-02-04 NaN 2000-01-05 2000-02-11
41 <NA> <NA> NaN 2002-06-21 2002-07-18
42 2002-12-05 2004-12-13 NaN 2005-11-02 2005-11-03
43 2002-03-08 2002-06-10 NaN 2002-05-14 2002-06-17
44 <NA> <NA> NaN 2014-08-14 <NA>
45 <NA> <NA> NaN 2014-09-29 <NA>
46 2001-10-08 2001-12-07 NaN 2002-07-09 2002-07-09
47 <NA> <NA> NaN 2010-10-16 2010-11-11
48 2002-07-28 <NA> NaN 2002-08-10 2002-09-11
49 2011-03-04 <NA> NaN 2014-10-08 <NA>
50 2006-06-05 2006-10-01 NaN 2014-09-01 <NA>
51 2001-11-28 2002-03-08 NaN 2002-02-22 2002-03-15
52 <NA> <NA> NaN 2014-09-24 <NA>
53 <NA> <NA> NaN 2014-08-07 <NA>
54 2012-04-22 <NA> 2008-05-08 2012-04-21 2012-05-20
55 1996-01-27 2000-04-15 NaN 2000-03-23 2000-04-22
56 <NA> <NA> NaN 2005-05-22 2005-06-17
57 <NA> <NA> NaN 2004-09-20 2004-10-26
58 1997-12-26 <NA> NaN 1997-12-09 1998-01-02
59 <NA> <NA> NaN 2014-11-01 <NA>
60 2003-01-04 2004-04-09 NaN 2009-12-23 2010-01-18
61 1999-02-21 2000-10-11 NaN 2000-09-28 2000-10-18
62 <NA> <NA> NaN 2006-10-14 2006-10-29
63 <NA> <NA> NaN 2014-12-30 <NA>
64 1996-02-05 <NA> NaN 1996-01-17 1996-02-12
65 2000-12-16 <NA> NaN 2001-01-01 2000-12-23
66 <NA> <NA> NaN 2000-09-17 2000-10-16
67 <NA> <NA> NaN 2014-09-25 <NA>
68 2009-03-07 <NA> NaN 2009-03-16 2009-03-29
69 2009-05-01 2010-02-11 NaN 2014-12-15 <NA>
70 <NA> <NA> NaN 2014-10-06 <NA>
71 1998-09-13 <NA> NaN 1998-09-11 1998-09-20
72 1998-05-27 1998-12-12 NaN 2001-08-05 2001-07-20
73 <NA> <NA> NaN 2008-11-12 2008-12-19
74 <NA> <NA> NaN 1996-09-09 1996-09-12
75 <NA> <NA> NaN 2014-09-09 <NA>
76 <NA> <NA> NaN 2014-12-30 <NA>
77 1995-09-16 1995-12-16 NaN 1999-07-30 1999-08-07
78 <NA> <NA> NaN 2014-12-30 <NA>
79 <NA> <NA> NaN 1999-12-16 1999-12-25
80 <NA> <NA> NaN 1999-03-13 1999-03-23
81 1994-12-12 <NA> NaN 1994-11-15 1994-12-19
82 2009-06-12 <NA> NaN 2009-05-16 2009-06-19
83 2009-11-28 2010-02-12 2009-12-19 2010-01-26 2010-02-27
84 <NA> <NA> NaN 2014-09-03 <NA>
85 2009-05-10 2009-05-20 2008-06-27 2009-06-11 2009-06-08
86 2005-05-11 2009-08-04 NaN 2014-06-24 <NA>
87 2009-08-12 <NA> NaN 2014-10-21 <NA>
88 2004-04-18 <NA> NaN 2004-03-25 2004-04-26
89 2005-01-23 <NA> NaN 2004-12-30 2005-01-30
90 <NA> <NA> NaN 2014-06-23 <NA>
91 <NA> <NA> NaN 1998-08-15 1998-08-29
92 2007-10-11 <NA> NaN 2007-10-05 2007-10-18
93 <NA> <NA> NaN 2004-04-13 2004-05-15
94 2001-06-19 <NA> NaN 2001-07-02 2001-06-26
95 <NA> <NA> NaN 2014-11-01 <NA>
96 2000-11-10 2012-03-24 NaN 2012-03-23 2012-03-31
97 <NA> <NA> NaN 2014-11-04 <NA>
98 <NA> <NA> NaN 2014-08-19 <NA>
99 <NA> <NA> NaN 1995-03-30 1995-05-02
100 <NA> <NA> NaN 2014-11-01 <NA>
101 <NA> <NA> 2013-03-17 2014-10-29 <NA>
102 <NA> <NA> NaN 2014-10-28 <NA>
103 <NA> <NA> NaN 2001-06-14 2001-07-07
104 <NA> <NA> NaN 2014-08-26 <NA>
105 <NA> <NA> NaN 2009-11-21 2009-12-16
106 <NA> <NA> NaN 2014-06-19 <NA>
107 <NA> <NA> NaN 2014-12-30 <NA>
108 <NA> <NA> NaN 1999-03-10 1999-03-21
109 1999-06-09 2002-12-17 2002-04-26 2002-12-19 2003-01-06
110 <NA> <NA> NaN 2014-11-11 <NA>
111 1999-05-07 2000-10-30 NaN 2003-03-22 2003-03-25
112 <NA> <NA> 2003-02-25 2006-04-06 2006-04-25
113 <NA> <NA> NaN 1998-03-26 1998-03-21
114 2005-01-21 2005-02-07 NaN 2005-03-06 2005-03-17
115 1998-03-02 2006-12-11 NaN 2006-11-23 <NA>
116 2014-03-26 <NA> NaN 2014-09-02 <NA>
117 <NA> <NA> NaN 2014-10-06 <NA>
118 2003-08-07 2004-05-31 1999-12-26 2005-05-19 2005-05-18
119 2003-07-21 <NA> NaN 2003-07-11 2003-07-28
120 2000-05-26 2007-01-12 NaN 2009-05-08 2009-06-04
121 2008-12-04 2009-06-16 NaN 2009-05-17 2009-06-23
122 2010-03-25 2011-01-28 NaN 2014-12-30 <NA>
123 1996-05-18 <NA> NaN 1996-05-16 1996-05-25
124 2002-08-31 <NA> NaN 2002-08-19 2002-09-07
125 2000-08-05 2005-07-19 2009-03-07 2009-10-08 2009-10-26
126 <NA> <NA> NaN 1998-08-19 1998-09-21
127 <NA> <NA> NaN 2014-06-25 <NA>
128 1998-04-20 <NA> NaN 1998-03-26 1998-04-27
129 2001-12-24 2005-01-29 NaN 2014-09-01 <NA>
130 2011-08-23 2012-01-12 NaN 2012-01-31 2012-02-19
131 <NA> <NA> NaN 2002-06-28 2002-06-14
132 2011-11-10 <NA> NaN 2011-11-30 2011-12-25
133 <NA> <NA> NaN 1999-03-29 1999-05-02
134 1996-05-22 1997-09-20 2000-04-27 2000-05-03 2000-05-21
135 <NA> <NA> NaN 2014-12-30 <NA>
136 <NA> <NA> NaN 2014-12-30 <NA>
137 <NA> <NA> NaN 2014-08-27 <NA>
138 <NA> <NA> NaN 2004-02-15 2004-03-06
139 2012-09-26 <NA> NaN 2014-10-02 <NA>
140 2004-06-30 2004-08-21 NaN 2004-09-13 2004-10-01
141 <NA> <NA> NaN 2014-10-09 <NA>
142 <NA> <NA> NaN 2014-06-16 <NA>
143 2011-01-25 <NA> NaN 2011-01-29 2011-02-01
144 <NA> <NA> NaN 2009-12-20 2009-12-31
145 <NA> <NA> NaN 2010-06-20 2010-07-07
146 1998-08-30 <NA> NaN 1998-09-13 1998-09-06
147 <NA> <NA> NaN 2014-11-04 <NA>
148 2004-06-23 <NA> NaN 2004-06-09 2004-06-30
149 <NA> <NA> NaN 2002-03-09 2002-04-13
150 2008-06-05 2008-07-05 NaN 2010-05-18 2010-05-04
151 <NA> <NA> NaN 2014-09-22 <NA>
152 2013-11-05 <NA> NaN 2014-10-28 <NA>
153 <NA> <NA> NaN 2014-07-30 <NA>
154 <NA> <NA> NaN 2014-10-14 <NA>
155 <NA> <NA> NaN 2014-10-28 <NA>
156 <NA> <NA> 2011-06-21 2012-01-20 2012-01-10
157 <NA> <NA> NaN 2014-10-09 <NA>
158 <NA> <NA> NaN 2014-09-29 <NA>
159 2013-02-16 <NA> NaN 2014-08-11 <NA>
160 <NA> <NA> NaN 2014-10-20 <NA>
[[2]]
id doV a1c chol crea
1 1 1993-05-07 87.3 3.9 83
2 2 1993-05-05 66.5 6.6 83
3 3 1993-05-11 73.0 5.6 68
4 4 1993-05-12 61.2 5.2 97
5 5 1993-02-25 102.7 6.0 149
6 6 1993-03-18 93.4 4.8 55
7 7 1993-03-16 94.5 8.6 56
8 8 1993-03-12 103.9 5.1 78
9 9 1993-03-16 46.4 4.2 123
10 10 1993-01-21 84.4 5.4 79
11 11 1993-01-28 47.9 8.9 74
12 12 1993-02-19 86.4 5.3 84
13 13 1993-03-23 80.9 6.0 65
14 14 1993-02-03 88.3 5.7 52
15 15 1993-01-19 83.8 6.3 61
16 16 1993-01-20 47.9 6.8 61
17 17 1993-05-19 78.0 5.7 52
18 18 1993-05-20 106.2 4.1 62
19 19 1993-05-11 53.1 9.5 98
20 20 1993-05-13 65.4 5.7 46
21 21 1993-05-23 73.0 4.9 82
22 22 1993-05-21 72.1 5.3 74
23 23 1993-01-25 88.8 6.6 79
24 24 1993-03-12 69.4 6.9 52
25 25 1993-03-04 65.6 7.4 88
26 26 1993-03-20 66.5 5.4 44
27 27 1993-03-24 76.2 4.0 71
28 28 1993-02-25 38.3 5.3 61
29 29 1993-04-16 113.7 4.7 54
30 30 1993-05-27 66.7 5.9 149
31 31 1993-03-28 88.1 7.8 99
32 32 1993-03-17 111.0 7.1 90
33 33 1993-03-10 35.8 3.3 91
34 34 1993-02-04 56.5 5.2 86
35 35 1993-06-28 56.3 6.8 88
36 36 1993-03-19 65.6 4.8 48
37 37 1993-01-29 51.5 5.8 71
38 38 1993-02-13 63.9 5.4 94
39 39 1993-02-04 56.2 5.9 67
40 40 1993-03-28 86.6 8.5 41
41 41 1993-01-22 51.0 5.1 137
42 42 1993-04-04 48.1 5.6 96
43 43 1993-02-11 70.6 6.2 73
44 44 1993-02-08 94.8 5.7 71
45 45 1993-02-23 52.8 4.9 55
46 47 1993-05-20 60.5 5.9 61
47 48 1993-06-02 44.6 4.0 47
48 50 1993-05-31 55.6 5.0 110
49 51 1993-06-01 79.3 6.5 63
50 52 1993-02-24 109.6 6.9 53
51 53 1993-03-04 40.4 7.1 62
52 54 1993-02-26 51.4 5.6 44
53 55 1993-04-28 85.8 5.6 60
54 56 1993-03-15 98.9 5.3 106
55 57 1993-01-24 76.6 7.0 28
56 58 1993-02-12 60.0 5.3 144
57 59 1993-06-04 74.8 5.5 62
58 60 1993-06-11 73.2 5.6 120
59 61 1993-06-10 43.1 4.1 72
60 62 1993-06-03 104.1 7.9 49
61 63 1993-04-02 107.9 6.2 85
62 64 1993-02-18 108.3 4.7 109
63 65 1993-04-19 92.4 4.7 71
64 66 1993-02-26 55.4 5.5 125
65 67 1993-04-14 67.8 6.0 84
66 68 1993-04-14 38.2 8.4 108
67 69 1993-03-01 48.6 3.2 112
68 70 1993-02-16 74.3 7.7 123
69 71 1993-04-18 97.5 6.7 121
70 72 1993-04-15 97.3 5.9 121
71 73 1993-02-09 89.6 5.7 65
72 74 1993-04-21 63.6 6.3 41
73 75 1993-02-28 71.6 6.7 85
74 76 1993-03-01 100.9 5.9 62
75 77 1993-04-23 52.9 6.8 99
76 78 1993-05-02 47.7 5.5 82
77 79 1993-05-01 82.9 6.5 108
78 80 1993-03-19 57.3 6.6 55
79 81 1993-03-24 99.8 6.2 79
80 82 1993-01-25 54.1 5.3 127
81 83 1993-01-13 53.8 4.9 77
82 84 1993-03-03 63.7 5.7 60
83 85 1993-04-26 56.5 5.7 45
84 86 1993-05-03 67.7 5.0 86
85 87 1993-04-19 54.2 4.6 90
86 88 1993-03-15 78.7 8.3 101
87 89 1993-06-20 61.2 4.0 82
88 90 1993-08-02 59.4 3.6 79
89 91 1993-07-25 69.5 4.5 48
90 92 1993-08-29 53.2 5.7 94
91 93 1993-08-05 73.3 5.4 124
92 94 1993-08-02 70.1 3.6 107
93 95 1993-08-27 56.7 5.2 109
94 96 1993-07-31 60.6 6.0 92
95 97 1993-08-01 45.5 4.6 80
96 98 1993-07-31 80.7 4.3 72
97 100 1993-08-10 88.2 7.7 90
98 101 1994-01-19 72.8 4.0 61
99 102 1993-08-11 60.4 4.2 60
100 103 1993-08-21 83.3 5.8 107
101 104 1993-08-22 69.5 4.7 80
102 105 1994-02-04 59.6 4.9 57
103 107 1993-08-27 63.7 7.0 54
104 108 1993-08-24 38.6 4.5 103
105 110 1993-08-24 42.4 4.8 52
106 111 1993-09-03 41.0 6.3 80
107 112 1993-10-09 74.3 6.8 69
108 113 1993-10-09 50.9 5.3 89
109 114 1993-10-08 83.6 5.8 127
110 115 1993-10-01 47.7 6.0 63
111 116 1993-10-08 88.1 5.2 62
112 118 1993-10-19 63.5 6.3 73
113 119 1993-10-10 52.9 7.4 78
114 120 1993-10-09 77.5 3.1 76
115 121 1993-10-18 48.9 4.6 83
116 123 1993-11-06 67.5 5.1 58
117 126 1993-10-27 70.4 5.7 87
118 128 1993-11-08 48.7 5.0 100
119 129 1993-11-01 91.5 4.4 93
120 130 1993-11-02 89.2 6.2 86
121 131 1993-11-02 48.4 4.1 98
122 133 1994-03-22 72.3 6.8 51
123 134 1993-11-09 82.6 5.5 87
124 135 1993-11-15 75.0 6.3 56
125 136 1993-11-20 100.1 5.7 94
126 137 1993-11-25 53.7 5.2 75
127 138 1993-11-28 52.0 4.7 47
128 139 1993-11-30 69.6 7.8 76
129 140 1993-12-10 76.9 5.7 58
130 142 1993-11-27 81.7 4.6 73
131 143 1993-12-10 69.3 4.6 93
132 144 1993-12-05 74.2 6.6 91
133 145 1994-01-14 65.1 4.5 57
134 146 1994-01-25 72.1 4.3 58
135 147 1994-01-25 48.6 4.6 126
136 148 1993-12-31 82.2 8.4 67
137 150 1994-01-13 57.6 6.4 47
138 151 1994-01-22 50.1 3.6 61
139 152 1994-01-25 89.5 6.8 95
140 153 1994-01-23 69.8 4.3 48
141 155 1994-01-21 46.5 3.5 126
142 156 1994-01-19 56.4 4.1 59
143 157 1994-03-15 81.2 6.4 66
144 158 1994-02-02 47.7 4.6 87
145 159 1994-01-22 64.0 4.4 62
146 160 1994-01-26 90.9 5.9 126
147 161 1994-01-24 65.6 4.9 83
148 163 1994-02-07 61.0 6.2 141
149 165 1994-02-21 66.5 6.7 66
150 166 1994-02-20 62.2 6.1 80
151 167 1994-02-17 73.6 6.0 96
152 168 1994-02-25 86.2 6.5 101
153 169 1994-05-02 51.7 5.3 62
154 170 1994-05-04 77.1 5.0 54
155 171 1994-04-30 61.2 5.7 108
156 172 1994-05-11 49.7 5.0 48
157 173 1994-05-14 70.5 4.1 59
158 174 1994-05-23 103.0 4.6 110
159 175 1994-05-30 51.3 4.3 106
160 176 1994-05-20 86.7 5.7 62
161 1 1995-06-25 68.9 4.2 73
162 2 1995-06-28 54.8 5.7 105
163 3 1995-07-01 79.6 4.1 78
164 4 1995-06-27 102.3 4.8 106
165 5 1995-07-07 54.7 8.8 140
166 6 1995-07-02 100.1 8.8 184
167 7 1995-07-04 103.4 6.0 60
168 8 1995-07-06 52.8 4.0 197
169 9 1995-06-24 73.7 3.3 216
170 10 1995-07-07 60.5 5.5 176
171 11 1995-06-28 63.2 4.9 91
172 12 1995-06-29 109.8 4.6 63
173 13 1995-07-05 55.1 4.9 67
174 14 1995-07-07 79.5 4.2 51
175 15 1995-07-04 59.5 4.8 123
176 16 1995-07-06 87.3 4.5 155
177 17 1995-07-06 66.1 3.6 49
178 18 1995-06-24 90.2 4.7 59
179 19 1995-07-02 74.4 8.7 72
180 20 1995-07-04 84.2 6.0 174
181 21 1995-06-25 94.7 4.9 75
182 22 1995-07-05 57.7 4.9 60
183 23 1995-06-24 110.4 5.9 167
184 24 1995-07-03 58.5 4.8 85
185 25 1995-06-25 80.4 7.4 75
186 26 1995-07-02 89.5 5.8 100
187 27 1995-07-05 58.0 3.8 61
188 28 1995-06-24 37.9 7.0 75
189 29 1995-06-27 97.4 3.2 82
190 30 1995-07-04 62.2 5.7 81
191 31 1995-06-26 91.8 6.1 149
192 32 1995-07-01 104.1 5.6 87
193 33 1995-06-26 45.9 4.0 83
194 34 1995-07-04 48.9 4.3 65
195 35 1995-07-02 109.4 7.5 109
196 36 1995-07-01 69.2 4.7 70
197 37 1995-06-26 47.6 3.8 61
198 38 1995-07-03 82.0 6.7 178
199 39 1995-07-06 71.1 4.5 123
200 40 1995-06-26 68.0 6.6 94
201 41 1995-07-03 43.5 4.5 97
202 42 1995-07-03 78.1 5.5 113
203 43 1995-06-28 80.0 7.1 138
204 44 1995-06-30 104.5 6.3 99
205 45 1995-06-29 61.1 4.5 63
206 47 1995-06-29 89.9 5.4 55
207 48 1995-06-29 57.9 3.2 79
208 50 1995-06-26 92.8 4.7 94
209 51 1995-06-27 66.7 5.5 81
210 52 1995-06-25 88.3 4.7 102
211 53 1995-06-26 59.0 6.9 48
212 54 1995-07-05 40.9 4.8 44
213 55 1995-06-25 57.0 4.7 61
214 56 1995-07-04 54.5 5.5 142
215 57 1995-07-02 69.0 5.6 35
216 58 1995-06-29 77.0 4.8 144
217 59 1995-06-26 97.0 4.5 43
218 60 1995-07-03 78.9 4.9 76
219 61 1995-06-30 48.8 3.8 105
220 62 1995-06-28 84.5 7.2 72
221 63 1995-06-29 82.8 5.4 56
222 64 1995-07-06 96.5 4.1 131
223 65 1995-07-02 81.2 4.7 54
224 66 1995-06-27 63.8 5.6 146
225 67 1995-06-25 61.5 5.1 78
226 68 1995-07-01 91.3 5.7 62
227 69 1995-06-28 75.8 4.0 93
228 70 1995-07-02 73.2 4.7 148
229 71 1995-06-26 71.3 6.1 75
230 72 1995-06-27 85.2 4.9 102
231 73 1995-06-27 71.2 4.8 101
232 74 1995-06-27 65.1 3.5 54
233 75 1995-07-04 81.4 5.3 109
234 76 1995-06-27 46.5 4.9 100
235 77 1995-07-05 50.9 5.3 85
236 78 1995-06-30 68.4 4.6 167
237 79 1995-06-24 68.2 5.5 138
238 80 1995-06-28 73.1 5.5 63
239 81 1995-06-29 78.5 6.1 86
240 82 1995-06-25 70.2 5.5 86
241 84 1995-07-03 81.5 5.1 68
242 85 1995-06-26 84.8 4.6 88
243 86 1995-07-01 61.4 7.1 65
244 87 1995-06-25 85.9 4.1 117
245 88 1995-07-05 74.8 3.9 99
246 89 1995-06-26 50.3 3.4 220
247 90 1995-07-03 90.1 4.5 52
248 91 1995-07-03 74.1 4.6 87
249 92 1995-06-25 57.5 4.9 81
250 93 1995-07-01 59.3 4.6 53
251 94 1995-07-04 78.7 3.9 72
252 95 1995-07-07 43.8 4.7 122
253 96 1995-07-07 65.5 4.4 103
254 97 1995-07-07 43.2 4.5 110
255 98 1995-07-01 67.5 4.2 116
256 100 1995-06-29 77.7 6.1 111
257 101 1995-06-24 62.3 4.3 61
258 103 1995-07-02 80.4 5.1 54
259 104 1995-06-24 66.3 5.6 68
260 105 1995-07-04 81.1 4.8 126
261 107 1995-07-01 60.2 5.8 53
262 108 1995-06-28 65.9 5.8 88
263 110 1995-06-25 36.3 4.7 111
264 111 1995-06-25 60.2 5.3 131
265 112 1995-06-26 95.0 5.7 57
266 113 1995-06-24 63.0 4.9 64
267 114 1995-07-06 97.8 5.9 121
268 115 1995-07-01 46.7 4.5 68
269 116 1995-06-24 109.3 5.2 88
270 118 1995-07-02 73.5 6.1 92
271 119 1995-07-07 52.6 4.0 67
272 120 1995-07-02 78.9 3.6 54
273 121 1995-07-03 61.0 5.1 118
274 123 1995-07-03 63.1 5.0 105
275 126 1995-06-29 74.1 5.8 114
276 129 1995-07-01 66.2 4.4 94
277 130 1995-06-29 87.0 5.9 136
278 131 1995-07-06 44.2 4.5 69
279 133 1995-07-03 44.4 6.1 83
280 134 1995-06-26 84.7 5.7 71
281 135 1995-07-07 64.7 4.3 55
282 136 1995-06-28 119.7 5.9 106
283 137 1995-06-25 52.6 4.5 106
284 138 1995-07-04 56.8 3.9 75
285 139 1995-07-01 61.9 5.8 125
286 140 1995-07-07 89.6 5.7 47
287 142 1995-07-01 121.1 4.5 62
288 143 1995-07-02 55.2 4.4 124
289 144 1995-06-30 92.6 7.0 138
290 145 1995-07-04 96.1 5.3 121
291 146 1995-07-03 72.3 4.4 115
292 147 1995-06-27 52.3 4.4 142
293 148 1995-07-07 101.7 6.5 45
294 151 1995-07-05 63.3 3.3 114
295 152 1995-06-30 73.1 6.2 101
296 153 1995-06-27 52.7 3.7 100
297 155 1995-06-28 46.3 3.8 96
298 156 1995-07-06 49.2 4.2 116
299 157 1995-07-05 80.6 5.5 66
300 158 1995-06-25 47.6 4.9 145
301 159 1995-07-05 50.5 5.0 67
302 160 1995-06-28 99.3 6.7 96
303 161 1995-06-30 57.2 4.5 110
304 163 1995-06-27 55.7 6.2 94
305 165 1995-07-05 58.3 6.1 63
306 166 1995-06-29 62.3 5.2 102
307 167 1995-07-01 67.5 5.1 50
308 168 1995-06-27 76.1 5.3 73
309 169 1995-07-05 44.4 5.5 85
310 170 1995-06-25 57.0 5.6 90
311 171 1995-06-24 49.5 5.0 103
312 172 1995-07-07 76.4 14.0 126
313 173 1995-07-03 57.7 3.9 59
314 174 1995-07-05 58.1 5.0 67
315 175 1995-06-25 60.0 3.9 86
316 176 1995-06-25 77.9 4.9 52
317 1 1997-06-24 63.7 4.4 63
318 2 1997-07-05 49.5 6.8 50
319 3 1997-07-07 73.0 4.7 83
320 4 1997-07-06 73.6 5.7 118
321 5 1997-07-01 41.9 5.8 141
322 6 1997-07-01 108.6 8.7 67
323 7 1997-06-30 88.4 5.8 53
324 8 1997-06-26 52.9 3.5 287
325 10 1997-06-24 75.8 5.8 73
326 12 1997-06-27 129.8 4.7 79
327 13 1997-06-25 65.2 4.7 83
328 14 1997-07-05 59.1 3.5 77
329 15 1997-07-03 49.9 3.5 74
330 16 1997-06-30 61.8 6.5 96
331 17 1997-07-02 61.9 3.8 60
332 18 1997-07-03 72.8 6.9 100
333 19 1997-06-27 36.1 7.0 82
334 20 1997-07-04 93.4 6.3 95
335 21 1997-07-03 52.8 5.0 74
336 22 1997-06-25 49.5 4.5 48
337 23 1997-06-29 42.3 4.7 158
338 24 1997-06-24 69.1 5.0 107
339 25 1997-07-07 81.4 6.8 85
340 26 1997-06-29 86.5 6.0 64
341 27 1997-06-28 58.0 4.4 75
342 28 1997-06-30 60.3 4.8 85
343 29 1997-07-03 110.4 4.5 112
344 30 1997-06-24 63.9 4.4 150
345 31 1997-06-30 83.5 6.4 69
346 32 1997-07-02 83.7 5.1 57
347 33 1997-07-07 50.5 3.8 92
348 34 1997-07-04 51.4 5.0 108
349 35 1997-07-06 76.6 7.3 148
350 36 1997-06-30 74.0 5.4 77
351 37 1997-06-27 61.0 4.6 70
352 38 1997-07-02 83.5 7.9 129
353 39 1997-07-05 62.1 5.1 112
354 40 1997-06-26 79.4 6.0 68
355 41 1997-06-28 37.5 4.3 151
356 42 1997-06-27 83.9 5.6 157
357 43 1997-07-06 96.5 7.1 166
358 44 1997-07-04 76.9 6.6 119
359 45 1997-07-06 88.8 4.8 56
360 47 1997-07-06 105.5 7.8 79
361 48 1997-06-29 71.8 2.8 71
362 50 1997-07-02 74.6 5.2 124
363 51 1997-07-02 55.0 4.5 86
364 52 1997-06-27 70.1 4.6 100
365 53 1997-06-25 66.4 6.1 74
366 54 1997-06-28 44.1 5.6 51
367 55 1997-07-01 49.2 5.2 51
368 56 1997-06-30 42.2 4.1 86
369 57 1997-07-05 101.1 6.7 54
370 58 1997-06-27 72.1 5.0 97
371 59 1997-07-03 92.5 4.8 80
372 60 1997-06-25 69.0 5.4 107
373 61 1997-06-28 63.7 3.9 88
374 62 1997-06-26 106.2 9.1 72
375 63 1997-06-24 68.6 5.7 61
376 64 1997-07-01 90.4 4.1 77
377 65 1997-06-25 78.2 4.1 85
378 67 1997-07-02 52.5 5.2 112
379 68 1997-07-05 56.9 4.8 71
380 69 1997-07-03 53.8 4.1 120
381 70 1997-06-27 80.1 5.8 123
382 71 1997-07-05 78.6 6.6 110
383 72 1997-07-07 79.1 6.0 58
384 73 1997-07-01 47.3 5.6 63
385 74 1997-07-02 50.2 5.0 102
386 75 1997-06-29 51.1 4.9 75
387 77 1997-06-24 56.2 5.2 112
388 78 1997-06-29 58.9 4.2 147
389 79 1997-06-25 73.0 4.9 137
390 80 1997-07-03 72.2 5.0 93
391 81 1997-06-24 70.0 5.1 82
392 82 1997-06-27 72.2 5.7 132
393 85 1997-07-06 87.1 3.4 91
394 86 1997-07-05 54.9 4.9 61
395 87 1997-07-02 74.5 4.1 65
396 88 1997-06-30 79.8 5.2 104
397 89 1997-07-03 48.5 4.0 191
398 90 1997-06-25 59.6 4.4 67
399 91 1997-07-02 61.4 3.7 79
400 92 1997-07-01 57.9 5.0 94
401 93 1997-06-25 82.6 5.3 50
402 94 1997-07-07 61.6 4.0 77
403 95 1997-07-03 68.0 4.6 147
404 96 1997-07-06 60.5 5.6 65
405 97 1997-07-03 56.0 4.8 49
406 98 1997-07-05 49.5 4.4 155
407 100 1997-06-25 81.9 7.8 77
408 101 1997-06-26 50.6 4.6 75
409 103 1997-07-06 59.7 4.5 106
410 104 1997-06-30 60.5 3.8 64
411 105 1997-06-30 51.4 4.6 92
412 107 1997-06-26 52.0 5.6 111
413 108 1997-06-29 46.9 4.0 81
414 110 1997-06-30 57.1 4.7 75
415 111 1997-07-02 48.6 5.7 74
416 112 1997-07-06 73.7 5.6 35
417 113 1997-06-28 78.5 5.7 101
418 114 1997-06-25 70.4 5.5 114
419 115 1997-07-02 47.8 5.2 100
420 116 1997-07-03 123.1 5.7 60
421 118 1997-07-01 48.9 6.3 69
422 119 1997-07-07 34.7 3.4 65
423 120 1997-06-30 65.0 3.4 90
424 121 1997-07-04 61.8 5.6 128
425 123 1997-07-02 79.0 5.1 89
426 126 1997-06-25 68.7 4.6 81
427 129 1997-06-29 70.5 4.3 116
428 130 1997-06-24 70.8 5.6 119
429 131 1997-07-07 47.3 5.2 90
430 133 1997-07-02 62.9 5.9 96
431 135 1997-06-27 98.8 5.2 56
432 136 1997-07-03 113.7 5.9 69
433 137 1997-06-24 34.3 4.9 120
434 138 1997-07-04 44.2 3.9 96
435 139 1997-06-25 65.6 5.7 133
436 140 1997-06-26 81.2 6.2 93
437 142 1997-06-30 68.9 4.3 103
438 143 1997-07-05 73.6 5.4 236
439 144 1997-06-27 77.0 6.2 138
440 145 1997-07-04 60.9 4.5 60
441 146 1997-06-26 72.3 5.2 66
442 148 1997-07-04 119.0 7.0 51
443 151 1997-07-01 85.4 4.4 104
444 152 1997-06-26 62.5 5.0 55
445 153 1997-06-30 45.6 3.8 106
446 155 1997-07-06 50.8 3.8 84
447 156 1997-06-29 59.4 4.3 75
448 157 1997-07-03 70.1 5.5 84
449 158 1997-06-27 54.4 4.4 109
450 159 1997-06-26 43.9 3.9 111
451 160 1997-06-24 93.7 5.9 99
452 161 1997-06-25 52.9 4.5 91
453 163 1997-06-29 42.8 5.1 117
454 165 1997-07-03 57.7 5.6 78
455 166 1997-06-27 85.6 5.6 87
456 167 1997-06-29 81.8 5.1 68
457 168 1997-07-04 78.9 6.7 92
458 169 1997-06-30 52.3 4.7 126
459 170 1997-07-05 59.8 4.6 61
460 171 1997-07-02 82.0 5.5 59
461 172 1997-06-28 92.3 5.6 85
462 173 1997-07-07 69.8 4.7 68
463 174 1997-06-26 52.6 4.8 118
464 175 1997-07-07 78.3 4.8 75
465 176 1997-06-24 91.3 6.3 86
466 1 2001-01-31 50.6 3.0 105
467 2 2001-10-29 69.4 4.2 63
468 3 2001-02-01 70.5 5.8 158
469 4 2001-01-28 74.4 4.2 141
470 6 2001-01-10 91.5 7.4 59
471 7 2001-01-17 57.6 5.7 70
472 8 2001-08-27 75.2 3.3 130
473 10 2001-01-14 63.4 4.7 124
474 11 2001-11-08 89.0 9.1 138
475 12 2001-04-07 76.3 5.7 196
476 13 2001-02-05 69.8 3.8 59
477 14 2001-01-09 58.3 4.3 150
478 15 2001-08-11 80.2 4.1 189
479 16 2001-01-09 93.1 5.6 167
480 17 2001-01-17 68.7 3.6 45
481 18 2001-02-03 80.0 4.6 107
482 19 2001-03-30 59.3 7.7 110
483 20 2001-02-08 90.4 6.7 65
484 21 2001-10-22 66.5 5.9 124
485 22 2001-03-26 53.2 2.7 99
486 24 2001-10-27 62.8 4.0 84
487 25 2001-01-24 114.1 7.9 128
488 26 2001-04-18 75.5 5.4 113
489 27 2001-04-05 41.8 6.8 132
490 28 2001-11-04 62.7 5.1 122
491 29 2001-09-04 76.6 5.0 83
492 30 2001-08-12 54.6 4.3 106
493 31 2001-03-29 73.5 5.2 112
494 33 2001-08-20 42.9 3.4 105
495 34 2001-01-16 37.7 3.6 154
496 35 2001-04-02 110.7 7.6 114
497 36 2001-08-27 64.0 6.3 61
498 37 2001-01-09 62.0 4.1 91
499 38 2001-01-08 85.1 3.9 161
500 39 2001-03-16 62.2 4.4 144
501 41 2001-04-25 44.7 3.8 156
502 42 2001-03-17 91.7 4.3 109
503 43 2001-03-25 76.8 5.6 243
504 45 2001-03-28 57.8 5.3 96
505 47 2001-01-11 147.6 7.9 95
506 50 2001-03-29 80.4 5.2 97
507 51 2001-04-07 62.5 4.3 73
508 52 2001-04-18 84.9 5.0 121
509 53 2001-01-18 69.0 5.6 98
510 54 2001-04-06 51.5 3.9 70
511 55 2001-03-31 52.1 4.6 58
512 56 2001-01-13 36.6 3.1 167
513 58 2001-01-19 79.2 5.7 76
514 59 2001-10-26 88.6 3.6 114
515 61 2001-04-07 51.3 3.9 88
516 62 2001-04-20 93.7 6.3 82
517 64 2001-03-31 80.9 4.4 85
518 65 2001-02-11 76.0 3.1 91
519 69 2001-01-18 75.9 3.5 117
520 70 2001-01-07 68.9 6.5 77
521 71 2001-01-27 86.1 7.4 157
522 72 2001-02-05 69.0 5.3 110
523 74 2001-05-08 45.5 3.6 119
524 75 2001-09-02 75.8 5.3 157
525 77 2001-01-12 35.7 3.6 129
526 78 2001-03-20 65.0 5.5 141
527 80 2001-01-25 67.8 6.8 97
528 85 2001-02-11 97.6 5.3 84
529 86 2001-01-28 65.3 4.5 70
530 87 2001-10-31 63.0 3.1 78
531 88 2001-03-21 76.9 4.3 168
532 89 2001-08-19 53.0 4.0 143
533 90 2001-10-07 99.7 3.8 75
534 91 2001-03-14 65.1 3.4 177
535 92 2001-02-06 56.3 3.6 69
536 94 2001-08-24 54.9 4.6 87
537 95 2001-08-29 50.5 3.0 110
538 96 2001-04-07 62.8 3.7 157
539 97 2001-04-01 57.9 3.2 70
540 98 2001-04-15 49.1 3.3 72
541 100 2001-09-09 68.7 4.5 105
542 101 2001-09-13 58.4 3.9 106
543 103 2001-02-02 58.1 3.2 132
544 104 2001-02-08 88.3 4.0 77
545 105 2001-09-15 50.6 3.3 65
546 107 2001-04-05 54.1 4.8 60
547 108 2001-04-02 52.7 4.0 67
548 110 2001-01-11 52.3 5.7 78
549 111 2001-09-10 70.4 5.1 114
550 112 2001-08-23 73.7 6.1 65
551 114 2001-10-25 61.1 3.1 179
552 115 2001-03-14 48.8 5.4 90
553 116 2001-10-18 44.2 5.7 162
554 118 2001-11-12 42.7 6.3 101
555 120 2001-09-12 54.4 3.7 74
556 121 2001-08-24 56.4 4.0 153
557 123 2001-08-30 51.6 4.4 132
558 126 2001-03-22 62.0 4.2 125
559 128 2001-07-12 NA 4.5 1067
560 129 2001-10-22 65.7 4.6 116
561 130 2001-08-15 74.8 5.3 85
562 131 2001-08-24 70.6 5.4 88
563 133 2001-10-09 85.5 5.6 100
564 135 2001-08-30 72.3 3.9 85
565 136 2001-08-14 93.7 4.0 146
566 138 2001-04-20 54.8 4.3 62
567 140 2001-12-09 50.2 7.9 84
568 142 2001-09-03 75.6 3.7 88
569 143 2001-04-14 75.5 3.5 305
570 144 2001-04-22 72.6 5.9 127
571 147 2001-10-31 67.0 4.0 112
572 148 2001-08-19 79.5 5.1 74
573 151 2001-09-10 64.7 4.3 64
574 152 2001-04-04 70.7 4.1 59
575 153 2001-08-13 57.1 2.7 70
576 155 2001-09-23 47.6 4.8 133
577 156 2001-08-23 57.8 4.3 79
578 158 2001-09-17 55.8 4.6 107
579 159 2001-08-08 49.8 3.8 100
580 161 2001-10-28 68.0 4.7 84
581 163 2001-08-12 50.9 3.7 75
582 165 2001-11-04 114.1 9.7 84
583 166 2001-09-04 75.0 6.2 95
584 167 2001-04-06 66.8 4.2 105
585 168 2001-05-08 74.8 5.4 144
586 169 2001-09-09 56.9 5.1 104
587 170 2001-08-25 73.4 3.9 69
588 171 2001-10-20 73.6 5.0 151
589 172 2001-04-09 69.5 6.6 57
590 173 2001-08-22 68.5 4.2 82
591 174 2001-09-12 83.4 4.1 63
592 175 2001-09-10 59.8 4.5 99
593 176 2001-10-21 79.9 5.6 70
594 1 2006-09-19 74.9 3.2 107
595 2 2006-11-17 57.9 3.5 87
596 6 2006-10-03 104.8 5.1 139
597 8 2006-11-22 73.6 3.7 446
598 10 2006-08-31 75.9 3.7 307
599 13 2006-10-17 76.2 3.6 38
600 14 2007-01-06 68.7 3.2 87
601 15 2006-12-07 74.9 4.2 198
602 16 2006-09-12 65.6 3.1 200
603 17 2006-10-05 72.7 3.5 129
604 18 2006-09-21 94.5 4.3 95
605 19 2006-12-03 61.6 4.7 45
606 20 2006-09-12 83.3 3.4 132
607 21 2007-01-24 64.4 4.1 155
608 22 2006-10-08 58.4 3.2 89
609 24 2006-11-17 63.6 3.5 74
610 25 2006-09-03 89.7 4.4 97
611 27 2006-12-12 58.7 3.3 104
612 28 2006-11-20 46.7 6.3 89
613 31 2006-10-09 79.6 5.3 81
614 33 2006-10-06 36.2 4.4 56
615 34 2006-09-10 63.4 3.5 136
616 36 2006-11-13 68.3 3.5 97
617 37 2006-10-23 60.7 3.2 65
618 39 2006-09-25 65.2 4.2 56
619 44 2007-01-19 93.0 4.0 115
620 45 2006-09-30 63.1 3.1 79
621 48 2006-11-24 50.6 3.3 106
622 51 2006-10-09 67.0 4.8 103
623 52 2006-11-22 94.9 2.9 54
624 54 2006-12-12 50.4 4.1 52
625 55 2006-10-09 66.2 4.2 34
626 56 2006-09-24 59.2 3.1 170
627 61 2006-11-02 51.0 4.2 151
628 62 2006-11-05 88.1 4.4 116
629 65 2006-10-06 80.2 3.0 45
630 69 2006-10-24 63.9 2.5 65
631 70 2006-11-20 68.3 4.1 160
632 71 2006-10-07 87.0 4.7 141
633 75 2006-11-10 62.4 3.9 115
634 77 2006-09-29 42.8 3.0 109
635 78 2006-12-10 58.8 3.1 154
636 80 2006-09-30 67.1 4.0 111
637 84 2006-12-09 67.7 4.2 193
638 85 2006-09-22 85.5 3.4 344
639 86 2006-11-02 66.0 3.6 48
640 87 2006-12-18 51.8 2.8 261
641 88 2006-09-04 80.3 3.6 142
642 89 2006-10-18 54.3 4.4 184
643 92 2006-08-29 60.6 5.4 53
644 94 2006-12-05 74.4 2.9 52
645 97 2006-12-18 42.5 3.4 109
646 98 2006-12-09 67.2 2.5 94
647 100 2006-12-13 79.0 5.2 102
648 101 2006-11-13 59.8 3.8 130
649 103 2006-10-24 60.5 3.5 160
650 104 2006-09-11 71.3 5.2 211
651 105 2006-12-10 55.9 2.9 66
652 108 2006-10-14 44.8 3.7 72
653 110 2007-01-14 46.5 4.4 71
654 111 2006-10-06 51.1 3.8 45
655 112 2006-12-23 83.4 3.5 63
656 115 2006-09-26 54.6 3.8 62
657 121 2006-11-24 55.3 4.8 95
658 123 2006-12-01 70.0 4.1 73
659 126 2006-10-04 61.1 3.6 98
660 130 2006-10-19 86.8 2.6 119
661 131 2006-11-23 55.6 2.7 104
662 133 2007-01-15 65.1 5.4 53
663 136 2007-01-16 117.4 3.2 195
664 138 2006-10-03 46.1 3.9 39
665 140 2007-01-09 82.2 5.6 48
666 142 2006-11-05 83.1 2.8 115
667 144 2006-11-27 81.8 3.9 119
668 148 2006-09-13 104.7 4.3 80
669 152 2006-11-06 70.3 4.1 83
670 155 2006-10-24 41.4 4.7 78
671 156 2006-11-03 56.9 2.9 78
672 157 2006-12-23 86.9 5.5 231
673 158 2007-01-18 50.0 3.7 189
674 159 2006-09-11 59.7 3.1 83
675 161 2006-11-11 63.2 3.8 65
676 166 2006-09-05 66.4 5.6 146
677 167 2006-11-01 70.8 3.6 70
678 168 2006-12-17 82.5 3.5 159
679 169 2006-11-07 48.3 4.3 77
680 170 2006-09-29 74.3 3.0 56
681 171 2006-11-17 67.7 3.0 91
682 172 2006-10-07 74.6 3.3 133
683 173 2006-10-12 73.0 5.7 44
684 174 2006-10-24 77.5 3.4 117
685 175 2006-11-20 57.6 5.3 150
686 176 2006-11-06 86.6 3.5 96
687 1 2014-10-06 32.8 4.2 171
688 2 2014-07-31 58.1 3.7 63
689 6 2014-09-01 43.6 6.0 555
690 13 2014-09-20 50.1 2.7 73
691 15 2014-12-04 75.5 3.7 213
692 18 2014-12-25 78.6 3.7 135
693 19 2014-12-25 45.7 6.1 99
694 20 2014-08-30 58.5 3.7 88
695 21 2014-10-19 53.4 3.3 120
696 22 2014-10-14 38.3 3.2 81
697 25 2014-08-07 63.8 4.8 255
698 28 2014-07-30 51.7 4.7 110
699 33 2014-10-30 58.8 3.2 73
700 34 2014-09-23 49.8 3.5 251
701 36 2014-12-02 48.3 3.6 77
702 39 2014-09-02 85.8 4.4 145
703 44 2014-08-18 NA NA 118
704 45 2014-09-28 64.3 3.6 60
705 51 2014-10-12 53.1 3.0 114
706 52 2014-09-02 57.2 3.6 82
707 54 2014-09-22 61.8 3.5 36
708 55 2014-08-12 44.7 4.2 74
709 61 2014-10-28 68.1 4.7 122
710 65 2014-12-31 NA 5.1 NA
711 69 2014-09-18 59.0 2.2 101
712 71 2014-12-09 47.7 2.8 109
713 72 2014-10-04 63.1 4.1 58
714 77 2014-09-05 37.7 3.6 176
715 78 2014-12-25 53.4 2.5 158
716 80 2015-01-03 81.7 2.4 95
717 86 2014-09-09 49.6 5.2 117
718 88 2014-06-28 39.1 3.1 157
719 89 2014-10-25 64.8 2.7 141
720 92 2014-06-18 61.2 5.7 72
721 97 2014-10-26 57.1 3.7 66
722 100 2014-11-02 38.3 5.5 69
723 101 2014-08-24 86.4 3.5 153
724 103 2014-11-01 49.0 3.5 139
725 104 2014-10-27 51.8 4.4 492
726 105 2014-11-01 41.8 3.4 86
727 108 2014-08-19 42.6 3.4 78
728 111 2014-06-21 53.8 5.1 56
729 112 2014-12-25 72.4 4.5 91
730 115 2014-11-16 42.7 3.5 74
731 123 2014-09-06 48.3 3.1 52
732 126 2014-10-10 56.7 NA NA
733 138 2014-06-29 78.3 4.7 135
734 140 2014-09-03 68.1 5.4 110
735 147 2014-12-29 56.5 3.2 146
736 148 2014-12-24 41.2 5.8 46
737 150 2014-08-23 NA NA 58
738 152 2014-09-26 108.5 6.3 43
739 155 2014-10-03 48.9 4.5 154
740 156 2014-06-10 38.9 3.4 106
741 161 2014-11-02 39.8 4.9 84
742 167 2014-09-18 37.6 4.8 48
743 168 2014-11-02 55.5 3.7 211
744 169 2014-08-04 60.3 3.9 47
745 170 2014-10-16 76.3 4.3 54
746 171 2014-10-31 53.3 3.4 136
747 173 2014-10-13 84.4 4.3 134
748 174 2014-09-25 62.0 3.1 74
749 175 2014-08-07 78.5 2.2 149
750 176 2014-10-16 86.3 3.9 126
[[3]]
id doTr state
1 1 1993-06-12 Mic
2 1 1995-05-13 Norm
3 1 2000-01-26 Mic
4 1 2001-12-26 Norm
5 1 2007-04-27 Mic
6 2 1993-10-15 Norm
7 2 1997-09-15 Mic
8 2 1997-10-14 Mac
9 2 2003-06-05 Mic
10 2 2009-05-29 Mic
11 3 1993-07-22 Mic
12 3 1995-07-06 Mic
13 3 1997-03-18 Mac
14 4 1994-09-05 Mic
15 4 1996-05-05 Mic
16 4 1999-03-12 Mic
17 5 1993-10-09 Mac
18 5 1997-01-28 Mac
19 6 1993-08-17 Norm
20 6 1996-02-16 Norm
21 6 1998-07-17 Mic
22 6 2004-05-28 Mac
24 7 1994-01-16 Norm
25 7 1995-07-09 Norm
26 7 1999-03-22 Norm
27 8 1994-05-14 Mac
28 8 1996-10-11 Mac
29 8 1997-12-25 Mac
30 8 2003-10-05 Mac
31 9 1993-07-10 Mic
32 10 1994-09-26 Mac
33 10 1995-10-30 Mac
34 10 1998-01-30 Mac
35 10 2004-08-30 Mac
36 11 1994-07-12 Mic
37 11 1996-12-23 Mac
38 12 1994-09-25 Mic
39 12 1995-05-02 Mic
40 12 1998-11-17 Mac
41 13 1993-12-25 Norm
42 13 1996-01-12 Mic
43 13 1998-11-21 Norm
44 13 2002-04-05 Mic
45 13 2014-08-14 Mic
46 14 1993-07-14 Mic
47 14 1996-05-07 Mic
48 14 1997-11-30 Mic
50 15 1994-05-15 Norm
51 15 1995-06-28 Norm
52 15 2000-07-12 Norm
53 15 2002-11-09 Norm
55 16 1994-07-16 Mic
56 16 1996-11-27 Mic
57 16 1997-03-27 Mac
58 16 2004-04-15 Mic
59 17 1993-09-08 Mic
60 17 1996-11-09 Mic
61 17 1999-10-20 Mac
62 17 2006-09-12 Mic
63 18 1994-07-08 Mic
64 18 1995-01-18 Mic
65 18 1998-02-01 Norm
66 18 2006-06-05 Norm
67 18 2013-10-18 Norm
68 19 1994-02-09 Mic
69 19 1997-02-03 Mic
70 19 2001-03-15 Mic
71 19 2001-09-20 Mac
73 20 1994-09-18 Mac
74 20 1995-03-13 Mic
75 20 1998-11-01 Mic
76 20 2001-02-12 Mic
77 20 2008-12-24 Mac
78 21 1994-04-29 Mic
79 21 1996-12-15 Mac
80 21 1998-05-01 Mic
81 21 2006-01-26 Mic
83 22 1993-06-09 Mic
84 22 1995-11-16 Norm
85 22 1997-09-05 Norm
86 22 2004-02-06 Mic
87 22 2007-01-15 Mic
88 23 1994-12-30 Mac
89 23 1996-10-16 Mac
90 24 1993-09-25 Mic
91 24 1996-07-23 Mic
92 24 1999-09-09 Norm
93 24 2005-06-07 Norm
94 25 1994-01-11 Mic
95 25 1997-01-09 Mic
96 25 1997-06-02 Mic
97 25 2002-01-05 Mic
98 25 2008-10-22 Mac
99 26 1994-02-17 Mic
100 26 1995-06-14 Mic
101 26 1999-10-03 Mac
102 27 1995-03-01 Mic
103 27 1995-09-09 Mic
104 27 2000-06-02 Mic
105 27 2005-05-06 Mic
106 28 1993-12-21 Mac
107 28 1996-12-26 Mac
108 28 2000-04-05 Mic
109 28 2003-04-21 Norm
110 28 2007-12-08 Mic
111 29 1993-12-20 Mic
112 29 1996-06-28 Mac
113 29 2000-10-17 Mac
114 30 1993-12-14 Mic
115 30 1995-08-30 Norm
116 30 1999-06-24 Norm
117 31 1994-10-03 Mic
118 31 1996-05-15 Mic
119 31 2001-03-03 Mic
120 31 2001-09-04 Mac
121 32 1994-07-13 Mac
122 32 1996-04-27 Mic
123 33 1994-08-02 Mic
124 33 1995-05-01 Norm
125 33 1997-08-27 Norm
126 33 2005-05-28 Norm
128 34 1993-05-28 Mic
129 34 1996-11-13 Mic
130 34 1998-07-23 Mic
131 34 2002-04-14 Mac
133 35 1995-02-12 Mic
134 35 1995-06-22 Mic
135 35 1999-07-25 Mac
136 36 1994-10-01 Mic
137 36 1995-02-02 Norm
138 36 2001-06-06 Mic
139 36 2002-03-22 Mic
140 36 2013-07-28 Mic
141 37 1993-10-06 Mic
142 37 1996-02-06 Mic
143 37 1998-05-17 Mic
144 37 2004-04-09 Mic
145 38 1994-02-23 Mac
146 38 1995-11-16 Mic
147 38 1999-09-13 Mac
148 39 1993-09-25 Mic
149 39 1996-11-16 Mic
150 39 1998-06-10 Norm
151 39 2001-11-17 Norm
152 39 2006-10-26 Norm
153 40 1994-09-03 Mic
154 40 1996-05-12 Mic
155 41 1994-09-01 Mic
156 41 1997-04-18 Norm
157 41 1998-07-07 Mic
158 42 1995-02-16 Mic
159 42 1995-08-08 Mic
160 42 1997-09-07 Mic
161 43 1993-11-24 Mac
162 43 1995-08-27 Mic
163 43 1998-02-05 Mac
164 44 1994-06-23 Mic
165 44 1996-09-28 Mic
166 44 2000-06-01 Mic
167 45 1993-12-24 Mic
168 45 1996-04-14 Mic
169 45 1998-08-17 Mac
170 45 2002-04-01 Mic
171 45 2009-10-22 Mic
172 47 1993-06-06 Mic
173 47 1997-01-09 Mic
174 47 2000-08-07 Mic
175 48 1993-09-02 Mic
176 48 1996-01-15 Mic
178 50 1995-02-20 Mic
179 50 1995-06-11 Mic
180 50 2001-01-28 Mac
181 51 1994-03-27 Norm
182 51 1995-07-17 Norm
183 51 1999-05-23 Norm
184 51 2001-04-14 Norm
185 51 2010-07-12 Mic
186 52 1993-12-19 Mic
187 52 1995-12-11 Mic
188 52 1997-10-03 Mic
189 52 2003-02-28 Mic
190 52 2009-10-26 Mic
191 53 1993-04-21 Mac
192 53 1996-07-29 Mic
193 53 1999-05-07 Mac
194 54 1994-06-09 Mic
195 54 1996-10-14 Norm
196 54 2000-10-11 Norm
197 54 2003-09-10 Norm
198 54 2008-05-10 Norm
199 55 1995-01-08 Norm
200 55 1995-09-05 Norm
201 55 2000-04-09 Mic
202 55 2005-08-27 Mic
204 56 1993-11-11 Norm
205 56 1996-05-17 Mic
206 56 1998-10-21 Mic
207 56 2004-08-19 Norm
208 57 1993-11-09 Mac
209 57 1996-01-22 Mic
210 58 1994-10-26 Mic
211 58 1997-01-13 Mic
212 58 2001-01-04 Mic
213 59 1993-11-19 Mac
214 59 1997-09-01 Mac
215 59 1998-02-25 Mac
216 60 1993-12-26 Norm
217 60 1996-05-26 Mic
218 61 1994-09-25 Mac
219 61 1995-05-08 Mac
220 61 2001-03-24 Mac
221 61 2005-03-18 Mic
223 62 1993-07-25 Norm
224 62 1995-10-20 Norm
225 62 1998-04-13 Mic
226 62 2001-06-07 Mac
227 63 1994-02-16 Mac
228 63 1997-02-07 Mac
229 64 1994-07-09 Mic
230 64 1995-12-12 Mic
231 64 2000-01-01 Mic
232 65 1994-08-28 Norm
233 65 1995-09-20 Norm
234 65 1999-09-30 Norm
235 65 2001-04-29 Mic
236 65 2012-07-05 Mic
237 66 1994-04-15 Mic
238 67 1993-10-11 Mic
239 67 1995-10-12 Mac
240 68 1995-10-07 Mic
241 68 1997-04-12 Mic
242 69 1993-09-19 Mic
243 69 1996-06-27 Mic
244 69 1998-03-12 Mic
245 69 2003-06-11 Mic
246 69 2012-01-09 Mic
247 70 1994-04-15 Mic
248 70 1996-06-17 Mic
249 70 1999-06-28 Mac
250 70 2002-03-01 Norm
251 71 1994-02-27 Mic
252 71 1995-11-03 Mic
253 71 2000-10-04 Mic
254 71 2005-11-14 Mic
255 71 2013-07-14 Norm
256 72 1995-03-04 Mic
257 72 1996-09-16 Mic
258 72 1999-04-28 Mic
260 73 1993-05-02 Mic
261 73 1997-07-24 Mic
262 74 1995-03-09 Mic
263 74 1997-05-02 Mic
264 74 2000-02-21 Mac
265 75 1993-12-08 Mic
266 75 1997-04-26 Mic
267 75 2001-02-04 Norm
268 75 2002-10-17 Mic
269 76 1993-10-18 Norm
270 77 1993-05-20 Mic
271 77 1996-10-31 Mic
272 77 1998-10-21 Norm
273 77 2001-07-28 Mic
274 77 2012-12-10 Norm
275 78 1993-12-10 Norm
276 78 1995-11-03 Norm
277 78 1998-04-10 Norm
278 78 2005-03-12 Norm
280 79 1994-04-24 Mic
281 79 1995-08-03 Norm
282 80 1993-11-04 Mic
283 80 1996-10-01 Mic
284 80 1999-10-05 Mic
285 80 2006-02-16 Mic
287 81 1994-11-20 Mic
288 81 1996-01-31 Mic
289 82 1993-05-17 Mic
290 82 1995-08-19 Mic
291 84 1993-09-13 Mic
292 84 2004-09-28 Mac
293 85 1993-10-02 Mic
294 85 1995-11-30 Mic
295 85 1997-06-24 Mac
296 85 2004-05-27 Mac
297 86 1993-12-06 Norm
298 86 1996-01-15 Norm
299 86 1997-11-10 Norm
300 86 2001-10-05 Norm
301 86 2014-07-30 Mac
302 87 1994-04-05 Mic
303 87 1996-08-23 Mic
304 87 1997-09-07 Mac
305 87 2002-05-17 Mac
306 88 1995-03-06 Mic
307 88 1995-11-30 Mic
308 88 1998-07-27 Mic
309 88 2004-04-18 Mic
310 88 2011-10-16 Mac
311 89 1993-12-12 Mic
312 89 1997-02-03 Mic
313 89 1999-01-17 Mic
314 89 2004-07-02 Mic
315 89 2008-01-27 Mic
316 90 1995-06-21 Mic
318 90 2000-03-22 Mac
319 91 1994-03-26 Mic
320 91 1996-05-02 Mic
321 91 2000-11-29 Mic
322 92 1994-09-01 Mic
323 92 1996-01-02 Mic
324 92 1998-08-12 Mic
325 92 2003-11-10 Mic
326 92 2012-06-19 Mac
327 93 1993-10-19 Norm
328 93 1996-12-10 Norm
329 94 1995-03-23 Mic
330 94 1995-11-04 Mic
331 94 1999-01-01 Norm
332 94 2002-02-03 Norm
333 95 1995-01-20 Mic
334 95 1996-09-13 Mic
335 95 2000-01-26 Mic
336 96 1994-02-08 Norm
337 96 1996-09-07 Norm
338 96 1998-08-03 Norm
339 97 1994-06-10 Norm
340 97 1996-02-24 Norm
341 97 1998-05-20 Mic
342 97 2002-01-19 Mic
344 98 1994-12-11 Norm
345 98 1995-09-03 Norm
346 98 2000-09-12 Mic
347 98 2002-07-26 Mic
348 100 1995-01-12 Mic
349 100 1996-11-28 Mic
350 100 1999-02-19 Mic
351 100 2004-02-08 Mic
353 101 1995-06-10 Mic
354 101 1996-08-16 Norm
355 101 1999-01-26 Norm
356 101 2001-11-25 Mic
357 101 2010-06-29 Mac
358 103 1993-10-17 Mic
359 103 1996-05-22 Mic
360 103 1998-09-25 Norm
361 103 2004-05-18 Mic
363 104 1994-12-05 Mic
364 104 1995-11-10 Mic
365 104 1999-08-20 Mic
366 104 2005-12-19 Mac
367 104 2009-01-21 Mac
368 105 1994-03-29 Mic
369 105 1995-10-28 Mic
370 105 1997-09-28 Mic
371 105 2005-01-26 Mic
372 105 2012-10-09 Mic
373 107 1994-08-29 Mic
374 107 1996-12-20 Mic
375 107 1997-10-31 Norm
376 108 1994-11-07 Norm
377 108 1995-10-10 Norm
378 108 1998-12-12 Norm
379 108 2005-01-26 Mic
380 108 2011-05-19 Norm
381 110 1994-02-16 Mic
382 110 1996-04-28 Norm
383 110 1999-08-26 Norm
384 110 2003-12-08 Mic
385 111 1995-02-26 Mic
386 111 1996-04-23 Mic
387 111 1998-10-01 Norm
388 111 2001-09-28 Norm
389 111 2007-07-21 Norm
390 112 1993-10-09 Mic
391 112 1996-09-21 Mic
392 112 1998-04-28 Mic
393 112 2004-07-29 Mic
394 112 2011-12-18 Mic
395 113 1994-12-12 Mic
396 113 1996-04-02 Norm
397 114 1994-07-25 Mic
398 114 1996-03-07 Mic
399 114 2000-08-11 Mic
400 115 1995-07-05 Norm
401 115 1997-02-25 Norm
402 115 1998-09-07 Norm
403 115 2002-07-16 Norm
404 115 2009-02-21 Norm
405 116 1994-02-23 Mic
406 116 1997-01-01 Mic
407 116 1997-09-06 Mic
408 118 1995-06-17 Mic
409 118 1996-12-29 Mac
410 118 1997-11-08 Mac
411 119 1994-06-22 Norm
412 119 1996-11-12 Norm
413 120 1994-01-19 Mic
414 120 1996-05-28 Mic
415 120 1999-03-04 Norm
416 121 1994-05-24 Mic
417 121 1996-06-11 Mac
418 121 2000-07-24 Mac
419 121 2005-08-03 Mic
420 123 1995-02-12 Mic
421 123 1996-11-16 Mic
422 123 1998-09-28 Norm
423 123 2004-11-21 Mic
424 123 2006-12-04 Norm
425 126 1994-12-13 Mic
426 126 1997-01-14 Mic
427 126 1998-06-24 Mic
428 126 2003-01-21 Mic
429 126 2012-01-06 Mic
431 129 1995-07-02 Mic
432 129 1997-08-19 Mic
433 129 1998-07-12 Norm
434 130 1994-06-09 Mic
435 130 1996-07-18 Mic
436 130 2000-11-29 Mac
437 130 2002-10-16 Norm
438 131 1995-08-11 Norm
439 131 1997-05-05 Norm
440 131 1997-10-04 Norm
441 131 2001-12-29 Mac
442 133 1995-09-29 Norm
444 133 1998-09-23 Norm
445 133 2003-01-01 Norm
446 134 1994-09-27 Mic
447 135 1995-03-16 Mic
448 135 1996-03-26 Mic
449 135 1999-02-25 Mic
450 136 1995-08-17 Mic
451 136 1997-03-20 Mac
452 136 1997-09-08 Norm
453 136 2001-12-06 Mac
454 137 1995-07-13 Norm
455 137 1995-11-22 Norm
456 138 1994-06-27 Norm
457 138 1997-03-28 Norm
458 138 1997-06-25 Norm
459 138 2004-03-25 Norm
460 138 2009-05-13 Norm
461 139 1995-07-07 Mic
462 139 1996-09-30 Mic
463 140 1994-10-24 Mic
464 140 1995-10-26 Norm
465 140 1999-06-04 Norm
467 140 2008-08-12 Mic
468 142 1995-08-05 Mic
469 142 1995-09-13 Mic
470 142 2001-08-03 Mic
471 142 2002-03-03 Mic
472 143 1993-12-31 Mac
473 143 1996-05-07 Mic
474 143 1999-06-01 Norm
475 144 1994-10-09 Mic
476 144 1996-03-10 Mic
477 144 2001-03-29 Mic
478 144 2005-11-30 Mic
479 145 1995-03-23 Mic
480 145 1997-07-17 Norm
481 146 1994-12-13 Mic
482 146 1997-06-08 Norm
483 147 1994-08-05 Mic
484 147 1996-08-16 Mic
485 147 2007-05-21 Mic
486 148 1995-02-21 Mic
487 148 1996-02-01 Mac
488 148 1998-09-05 Mic
489 148 2003-01-17 Norm
491 151 1995-05-10 Mic
492 151 1997-06-12 Mac
493 151 1998-05-09 Mac
494 152 1994-10-31 Mic
495 152 1996-05-26 Mic
496 152 2000-06-22 Mic
497 152 2003-11-23 Mic
498 152 2009-03-19 Mic
499 153 1994-05-08 Mac
500 153 1997-04-18 Mac
501 153 1999-09-13 Mic
502 155 1995-02-07 Mic
503 155 1997-01-07 Mac
504 155 2000-03-22 Mic
505 155 2005-05-17 Mac
506 155 2007-06-20 Mac
507 156 1995-06-23 Mic
508 156 1997-07-30 Mic
509 156 1998-12-06 Mic
510 156 2001-10-05 Mic
511 156 2010-12-26 Mac
512 157 1994-05-16 Mic
513 157 1996-02-21 Mac
514 157 2006-10-26 Mac
515 158 1995-04-07 Mic
516 158 1997-09-19 Mic
517 158 2001-02-04 Mac
518 158 2005-09-20 Mic
519 159 1994-02-28 Mic
520 159 1996-10-28 Norm
521 159 1999-03-14 Mic
522 159 2006-07-09 Mac
523 160 1994-10-15 Mic
524 160 1996-06-06 Norm
525 161 1995-05-03 Mic
526 161 1995-12-21 Mic
527 161 1999-03-08 Mic
528 161 2003-07-10 Mic
529 161 2013-01-28 Mac
530 163 1994-12-20 Mic
531 163 1996-12-18 Mac
532 163 2000-04-21 Mac
533 165 1995-01-29 Norm
534 165 1996-01-01 Norm
535 165 1999-09-20 Mic
536 166 1994-12-05 Mic
537 166 1996-07-01 Mic
538 166 2000-03-17 Norm
539 166 2003-01-16 Mic
540 167 1994-03-30 Norm
541 167 1996-04-29 Norm
542 167 1998-05-16 Norm
543 167 2006-06-29 Norm
544 167 2010-08-28 Norm
545 168 1994-03-19 Mic
546 168 1996-04-17 Mac
547 168 1999-07-17 Mac
548 168 2003-04-10 Mac
550 169 1995-05-02 Mic
551 169 1995-12-18 Norm
552 169 1998-08-24 Norm
553 169 2005-10-28 Mic
554 169 2013-05-23 Mic
555 170 1994-09-07 Mic
556 170 1997-04-03 Norm
557 170 2000-02-07 Norm
558 170 2006-01-09 Mic
560 171 1994-10-09 Mic
561 171 1996-05-23 Norm
562 171 2001-09-24 Mac
563 171 2004-08-24 Mac
564 171 2011-04-13 Mic
565 172 1994-06-23 Mic
566 172 1996-02-12 Mic
567 172 1998-04-02 Mac
568 172 2002-02-23 Mac
569 173 1995-04-26 Norm
570 173 1996-06-12 Norm
571 173 1999-11-21 Norm
572 173 2002-09-08 Norm
574 174 1995-02-05 Mic
576 174 2000-07-25 Mic
577 174 2003-04-10 Mic
578 174 2009-04-16 Mic
579 175 1994-09-19 Mic
580 175 1997-02-27 Mic
581 175 1998-10-13 Mic
582 175 2004-08-02 Norm
583 175 2014-06-21 Mac
584 176 1994-11-10 Norm
585 176 1997-02-08 Norm
586 176 1998-12-07 Norm
587 176 2005-09-17 Norm
588 176 2013-05-22 Norm[[1]]
records max_id
1 160 176
[[2]]
records max_id
1 160 176
[[3]]
records max_id
1 156 176[[1]]
id allo sex baseCVD deathCVD doBth doDM doBase doCVD1
1 83 Int M 0 1 1930-12-23 1989-06-28 1992-12-22 1994-12-04
2 102 Int M 0 1 1935-04-09 1990-03-19 1993-07-17 1995-04-23
3 128 Conv M 0 0 1937-03-14 1989-02-16 1993-10-03 1995-11-13
4 150 Conv F 0 0 1946-02-18 1989-10-29 1993-12-24 <NA>
doCVD2 doCVD3 doESRD doEnd doDth
1 1994-12-12 <NA> NaN 1994-11-15 1994-12-19
2 <NA> <NA> NaN 1995-03-30 1995-05-02
3 2003-08-07 2004-05-31 1999-12-26 2005-05-19 2005-05-18
4 <NA> <NA> NaN 2014-08-27 <NA>
[[2]]
[1] id allo sex baseCVD deathCVD doBth doDM doBase
[9] doCVD1 doCVD2 doCVD3 doESRD doEnd doDth
<0 rows> (or 0-length row.names) id allo sex baseCVD deathCVD
Min. : 1.00 Int :80 F: 41 Min. :0.0000 Min. :0.0000
1st Qu.: 40.75 Conv:80 M:119 1st Qu.:0.0000 1st Qu.:0.0000
Median : 82.50 Median :0.0000 Median :0.0000
Mean : 85.31 Mean :0.1375 Mean :0.2375
3rd Qu.:130.25 3rd Qu.:0.0000 3rd Qu.:0.0000
Max. :176.00 Max. :1.0000 Max. :1.0000
doBth doDM doBase
Min. :1926-07-13 Min. :1962-06-21 Min. :1992-12-22
1st Qu.:1932-07-18 1st Qu.:1982-10-01 1st Qu.:1993-03-23
Median :1937-03-12 Median :1987-05-02 Median :1993-06-11
Mean :1938-06-03 Mean :1986-01-15 Mean :1993-07-22
3rd Qu.:1944-11-28 3rd Qu.:1990-05-03 3rd Qu.:1993-11-15
Max. :1956-04-07 Max. :1994-02-07 Max. :1994-06-14
doCVD1 doCVD2 doCVD3
Min. :1993-03-17 Min. :1994-08-05 Min. :1995-12-16
1st Qu.:1997-02-16 1st Qu.:1999-04-18 1st Qu.:2001-11-23
Median :2001-04-03 Median :2002-12-20 Median :2004-12-13
Mean :2001-12-18 Mean :2003-10-04 Mean :2005-01-11
3rd Qu.:2006-07-20 3rd Qu.:2008-07-20 3rd Qu.:2009-06-03
Max. :2014-05-30 Max. :2014-03-26 Max. :2012-07-21
NA's :41 NA's :84 NA's :117
doESRD doEnd doDth
Min. :1998-02-18 Min. :1994-11-16 Min. :1994-12-19
1st Qu.:2002-09-25 1st Qu.:2002-09-18 1st Qu.:2000-05-21
Median :2008-05-08 Median :2010-02-11 Median :2004-03-06
Mean :2006-07-17 Mean :2008-08-29 Mean :2004-05-24
3rd Qu.:2009-07-29 3rd Qu.:2014-09-25 3rd Qu.:2009-06-04
Max. :2014-07-07 Max. :2014-12-31 Max. :2014-02-16
NA's :145 NA's :67 `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 454 rows containing non-finite outside the scale range
(`stat_bin()`).
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 454 rows containing non-finite outside the scale range
(`stat_bin()`).
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 454 rows containing non-finite outside the scale range
(`stat_bin()`).
Call: survfit(formula = Surv(time_yrs, event_num) ~ 1, data = steno2)
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 160 0 1.000 0.00000 1.000 1.000
1 160 0 1.000 0.00000 1.000 1.000
2 158 2 0.988 0.00878 0.970 1.000
3 155 3 0.969 0.01376 0.942 0.996
4 154 1 0.963 0.01502 0.934 0.992
5 148 6 0.925 0.02082 0.885 0.967
6 143 5 0.894 0.02436 0.847 0.943
7 137 6 0.856 0.02774 0.804 0.912
8 131 6 0.819 0.03045 0.761 0.881
9 125 6 0.781 0.03268 0.720 0.848
10 116 9 0.725 0.03530 0.659 0.798
Call: survfit(formula = Surv(time_yrs, event_num) ~ allo, data = steno2)
allo=Int
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 80 0 1.000 0.0000 1.000 1.000
1 80 0 1.000 0.0000 1.000 1.000
2 78 2 0.975 0.0175 0.941 1.000
3 77 1 0.963 0.0212 0.922 1.000
4 76 1 0.950 0.0244 0.903 0.999
5 75 1 0.938 0.0271 0.886 0.992
6 73 2 0.913 0.0316 0.853 0.977
7 70 3 0.875 0.0370 0.805 0.951
8 66 4 0.825 0.0425 0.746 0.913
9 64 2 0.800 0.0447 0.717 0.893
10 63 1 0.788 0.0457 0.703 0.882
allo=Conv
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 80 0 1.000 0.0000 1.000 1.000
1 80 0 1.000 0.0000 1.000 1.000
2 80 0 1.000 0.0000 1.000 1.000
3 78 2 0.975 0.0175 0.941 1.000
4 78 0 0.975 0.0175 0.941 1.000
5 73 5 0.913 0.0316 0.853 0.977
6 70 3 0.875 0.0370 0.805 0.951
7 67 3 0.838 0.0412 0.760 0.922
8 65 2 0.813 0.0436 0.731 0.903
9 61 4 0.763 0.0476 0.675 0.862
10 53 8 0.663 0.0529 0.567 0.775
Call: survfit(formula = Surv(time_yrs, event_num) ~ sex, data = steno2)
sex=F
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 41 0 1.000 0.0000 1.000 1.000
1 41 0 1.000 0.0000 1.000 1.000
2 41 0 1.000 0.0000 1.000 1.000
3 40 1 0.976 0.0241 0.930 1.000
4 40 0 0.976 0.0241 0.930 1.000
5 40 0 0.976 0.0241 0.930 1.000
6 39 1 0.951 0.0336 0.888 1.000
7 37 2 0.902 0.0463 0.816 0.998
8 34 3 0.829 0.0588 0.722 0.953
9 31 3 0.756 0.0671 0.635 0.900
10 29 2 0.707 0.0711 0.581 0.861
sex=M
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 119 0 1.000 0.0000 1.000 1.000
1 119 0 1.000 0.0000 1.000 1.000
2 117 2 0.983 0.0118 0.960 1.000
3 115 2 0.966 0.0165 0.935 0.999
4 114 1 0.958 0.0184 0.923 0.995
5 108 6 0.908 0.0266 0.857 0.961
6 104 4 0.874 0.0304 0.816 0.936
7 100 4 0.840 0.0336 0.777 0.909
8 97 3 0.815 0.0356 0.748 0.888
9 94 3 0.790 0.0373 0.720 0.867
10 87 7 0.731 0.0406 0.656 0.815
Call: survfit(formula = Surv(time_yrs, event_num) ~ allo + sex, data = steno2)
allo=Int, sex=F
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 17 0 1.000 0.0000 1.000 1
1 17 0 1.000 0.0000 1.000 1
2 17 0 1.000 0.0000 1.000 1
3 17 0 1.000 0.0000 1.000 1
4 17 0 1.000 0.0000 1.000 1
5 17 0 1.000 0.0000 1.000 1
6 17 0 1.000 0.0000 1.000 1
7 17 0 1.000 0.0000 1.000 1
8 15 2 0.882 0.0781 0.742 1
9 14 1 0.824 0.0925 0.661 1
10 14 0 0.824 0.0925 0.661 1
allo=Int, sex=M
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 63 0 1.000 0.0000 1.000 1.000
1 63 0 1.000 0.0000 1.000 1.000
2 61 2 0.968 0.0221 0.926 1.000
3 60 1 0.952 0.0268 0.901 1.000
4 59 1 0.937 0.0307 0.878 0.999
5 58 1 0.921 0.0341 0.856 0.990
6 56 2 0.889 0.0396 0.815 0.970
7 53 3 0.841 0.0460 0.756 0.937
8 51 2 0.810 0.0495 0.718 0.913
9 50 1 0.794 0.0510 0.700 0.900
10 49 1 0.778 0.0524 0.682 0.888
allo=Conv, sex=F
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 24 0 1.000 0.0000 1.000 1.000
1 24 0 1.000 0.0000 1.000 1.000
2 24 0 1.000 0.0000 1.000 1.000
3 23 1 0.958 0.0408 0.882 1.000
4 23 0 0.958 0.0408 0.882 1.000
5 23 0 0.958 0.0408 0.882 1.000
6 22 1 0.917 0.0564 0.813 1.000
7 20 2 0.833 0.0761 0.697 0.997
8 19 1 0.792 0.0829 0.645 0.972
9 17 2 0.708 0.0928 0.548 0.916
10 15 2 0.625 0.0988 0.458 0.852
allo=Conv, sex=M
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 56 0 1.000 0.0000 1.000 1.000
1 56 0 1.000 0.0000 1.000 1.000
2 56 0 1.000 0.0000 1.000 1.000
3 55 1 0.982 0.0177 0.948 1.000
4 55 0 0.982 0.0177 0.948 1.000
5 50 5 0.893 0.0413 0.815 0.978
6 48 2 0.857 0.0468 0.770 0.954
7 47 1 0.839 0.0491 0.748 0.941
8 46 1 0.821 0.0512 0.727 0.928
9 44 2 0.786 0.0548 0.685 0.901
10 38 6 0.679 0.0624 0.567 0.813[[1]]
Call: survfit(formula = Surv(time_yrs, event_num) ~ 1, data = steno2)
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 160 0 1.000 0.00000 1.000 1.000
1 160 0 1.000 0.00000 1.000 1.000
2 158 2 0.988 0.00878 0.970 1.000
3 155 3 0.969 0.01376 0.942 0.996
4 154 1 0.963 0.01502 0.934 0.992
5 148 6 0.925 0.02082 0.885 0.967
6 143 5 0.894 0.02436 0.847 0.943
7 137 6 0.856 0.02774 0.804 0.912
8 131 6 0.819 0.03045 0.761 0.881
9 125 6 0.781 0.03268 0.720 0.848
10 116 9 0.725 0.03530 0.659 0.798
[[2]]
Call: survfit(formula = Surv(time_yrs, event_num) ~ allo, data = steno2)
allo=Int
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 80 0 1.000 0.0000 1.000 1.000
1 80 0 1.000 0.0000 1.000 1.000
2 78 2 0.975 0.0175 0.941 1.000
3 77 1 0.963 0.0212 0.922 1.000
4 76 1 0.950 0.0244 0.903 0.999
5 75 1 0.938 0.0271 0.886 0.992
6 73 2 0.913 0.0316 0.853 0.977
7 70 3 0.875 0.0370 0.805 0.951
8 66 4 0.825 0.0425 0.746 0.913
9 64 2 0.800 0.0447 0.717 0.893
10 63 1 0.788 0.0457 0.703 0.882
allo=Conv
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 80 0 1.000 0.0000 1.000 1.000
1 80 0 1.000 0.0000 1.000 1.000
2 80 0 1.000 0.0000 1.000 1.000
3 78 2 0.975 0.0175 0.941 1.000
4 78 0 0.975 0.0175 0.941 1.000
5 73 5 0.913 0.0316 0.853 0.977
6 70 3 0.875 0.0370 0.805 0.951
7 67 3 0.838 0.0412 0.760 0.922
8 65 2 0.813 0.0436 0.731 0.903
9 61 4 0.763 0.0476 0.675 0.862
10 53 8 0.663 0.0529 0.567 0.775
[[3]]
Call: survfit(formula = Surv(time_yrs, event_num) ~ sex, data = steno2)
sex=F
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 41 0 1.000 0.0000 1.000 1.000
1 41 0 1.000 0.0000 1.000 1.000
2 41 0 1.000 0.0000 1.000 1.000
3 40 1 0.976 0.0241 0.930 1.000
4 40 0 0.976 0.0241 0.930 1.000
5 40 0 0.976 0.0241 0.930 1.000
6 39 1 0.951 0.0336 0.888 1.000
7 37 2 0.902 0.0463 0.816 0.998
8 34 3 0.829 0.0588 0.722 0.953
9 31 3 0.756 0.0671 0.635 0.900
10 29 2 0.707 0.0711 0.581 0.861
sex=M
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 119 0 1.000 0.0000 1.000 1.000
1 119 0 1.000 0.0000 1.000 1.000
2 117 2 0.983 0.0118 0.960 1.000
3 115 2 0.966 0.0165 0.935 0.999
4 114 1 0.958 0.0184 0.923 0.995
5 108 6 0.908 0.0266 0.857 0.961
6 104 4 0.874 0.0304 0.816 0.936
7 100 4 0.840 0.0336 0.777 0.909
8 97 3 0.815 0.0356 0.748 0.888
9 94 3 0.790 0.0373 0.720 0.867
10 87 7 0.731 0.0406 0.656 0.815
[[4]]
Call: survfit(formula = Surv(time_yrs, event_num) ~ allo + sex, data = steno2)
allo=Int, sex=F
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 17 0 1.000 0.0000 1.000 1
1 17 0 1.000 0.0000 1.000 1
2 17 0 1.000 0.0000 1.000 1
3 17 0 1.000 0.0000 1.000 1
4 17 0 1.000 0.0000 1.000 1
5 17 0 1.000 0.0000 1.000 1
6 17 0 1.000 0.0000 1.000 1
7 17 0 1.000 0.0000 1.000 1
8 15 2 0.882 0.0781 0.742 1
9 14 1 0.824 0.0925 0.661 1
10 14 0 0.824 0.0925 0.661 1
allo=Int, sex=M
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 63 0 1.000 0.0000 1.000 1.000
1 63 0 1.000 0.0000 1.000 1.000
2 61 2 0.968 0.0221 0.926 1.000
3 60 1 0.952 0.0268 0.901 1.000
4 59 1 0.937 0.0307 0.878 0.999
5 58 1 0.921 0.0341 0.856 0.990
6 56 2 0.889 0.0396 0.815 0.970
7 53 3 0.841 0.0460 0.756 0.937
8 51 2 0.810 0.0495 0.718 0.913
9 50 1 0.794 0.0510 0.700 0.900
10 49 1 0.778 0.0524 0.682 0.888
allo=Conv, sex=F
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 24 0 1.000 0.0000 1.000 1.000
1 24 0 1.000 0.0000 1.000 1.000
2 24 0 1.000 0.0000 1.000 1.000
3 23 1 0.958 0.0408 0.882 1.000
4 23 0 0.958 0.0408 0.882 1.000
5 23 0 0.958 0.0408 0.882 1.000
6 22 1 0.917 0.0564 0.813 1.000
7 20 2 0.833 0.0761 0.697 0.997
8 19 1 0.792 0.0829 0.645 0.972
9 17 2 0.708 0.0928 0.548 0.916
10 15 2 0.625 0.0988 0.458 0.852
allo=Conv, sex=M
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 56 0 1.000 0.0000 1.000 1.000
1 56 0 1.000 0.0000 1.000 1.000
2 56 0 1.000 0.0000 1.000 1.000
3 55 1 0.982 0.0177 0.948 1.000
4 55 0 0.982 0.0177 0.948 1.000
5 50 5 0.893 0.0413 0.815 0.978
6 48 2 0.857 0.0468 0.770 0.954
7 47 1 0.839 0.0491 0.748 0.941
8 46 1 0.821 0.0512 0.727 0.928
9 44 2 0.786 0.0548 0.685 0.901
10 38 6 0.679 0.0624 0.567 0.813[[1]]
[[2]]
[[3]]
[[4]]
Warning in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : All aesthetics have length 1, but the data has 3 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.Warning in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : All aesthetics have length 1, but the data has 3 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.
All aesthetics have length 1, but the data has 3 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.
All aesthetics have length 1, but the data has 3 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.
Call:
survdiff(formula = Surv(time_yrs, event_num) ~ allo, data = steno2)
N Observed Expected (O-E)^2/E (O-E)^2/V
allo=Int 80 38 51.7 3.64 8.29
allo=Conv 80 55 41.3 4.56 8.29
Chisq= 8.3 on 1 degrees of freedom, p= 0.004 Call:
survdiff(formula = Surv(time_yrs, event_num) ~ sex, data = steno2)
N Observed Expected (O-E)^2/E (O-E)^2/V
sex=F 41 20 25.5 1.182 1.63
sex=M 119 73 67.5 0.446 1.63
Chisq= 1.6 on 1 degrees of freedom, p= 0.2 
# A tibble: 4 × 8
model term estimate std.error statistic p.value conf.low conf.high
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 allo alloConv 0.602 0.212 2.84 0.00455 0.186 1.02
2 sex sexM 0.321 0.253 1.27 0.203 -0.174 0.816
3 both alloConv 0.628 0.213 2.95 0.00316 0.211 1.05
4 both sexM 0.379 0.253 1.50 0.135 -0.118 0.875# A tibble: 4 × 8
model term estimate std.error statistic p.value conf.low conf.high
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 allo alloConv 1.83 0.212 2.84 0.00455 1.20 2.77
2 sex sexM 1.38 0.253 1.27 0.203 0.840 2.26
3 both alloConv 1.87 0.213 2.95 0.00316 1.23 2.84
4 both sexM 1.46 0.253 1.50 0.135 0.889 2.40# A tibble: 2 × 3
allo .fitted .se.fit
<chr> <dbl> <dbl>
1 Conv 1.83 0.287
2 Int 1 0 # A tibble: 4 × 4
allo sex .fitted .se.fit
<fct> <fct> <dbl> <dbl>
1 Conv M 2.74 0.567
2 Int M 1.46 0.306
3 Conv F 1.87 0.291
4 Int F 1 0